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Job Description Senior Network Engineer, Trading Infrastructure Location: Chicago Type: Full time, Permanent Team: Network Engineering / Network Automation A leading global trading firm is seeking a Senior Network Engineer to design, build, operate and evolve the network infrastructure behind its trading business. This is a hands-on role for an engineer who enjoys solving hard problems across low latency networking, automation, WAN connectivity and global data centre environments. The successful candidate will work on critical projects that help the firm scale, improve performance and stay ahead in a highly competitive trading environment. No financial markets experience is required. Strong networking expertise, technical curiosity and a drive to improve complex systems matter most. The opportunity The network is central to the firm's trading platform. The successful candidate will help ensure that global connectivity, colocation environments and data centre networks remain fast, resilient, observable and operationally efficient. The role offers broad exposure to a demanding network estate, spanning low latency trading infrastructure, global WAN links, data centre networking and colocation connectivity. The successful candidate will have real ownership of meaningful projects, from identifying improvements through to design, implementation and operational support. What the role involves Deploy, monitor, troubleshoot and manage network infrastructure across global data centres, colocation sites and WAN environments. Design new colocation deployments and improve connectivity, resilience and performance across existing colocation environments. Improve the design, performance and scalability of the firm's data centre network. Identify, recommend and implement network optimisations that improve speed, reliability and operational efficiency. Support planned maintenance activity, including migrations, expansions, hardware upgrades and software upgrades. Provide technical expertise and support to a high performance trading floor operating in a competitive market making environment. Ensure WAN connectivity is resilient, performant and effectively integrated across the firm's global data centre footprint. Contribute to capacity planning, traffic flow optimisation and long term network scalability. Advance the firm's approach to intelligent network automation, reducing manual effort and improving consistency. Tackle technical debt, simplify complex environments and improve the maintainability of network systems. Who the firm is looking for The ideal candidate is a strong network engineer with broad experience across enterprise and performance critical environments. They will be comfortable working across LAN and global WAN networks, troubleshooting complex issues and improving systems through automation and thoughtful design. Strong network and performance monitoring skills, with experience in network capacity planning. Experience working across LAN and global WAN environments. Experience with Cisco and Arista network equipment is preferred. Strong troubleshooting skills across network transport protocols, including TCP and UDP. Deep understanding of routing protocols, including OSPF and BGP. Strong knowledge of network protocols, including Multicast, Spanning Tree and HSRP. Experience with network automation and strong Python scripting skills are preferred. Experience with network monitoring and alerting systems. A belief that clear documentation is essential to successful project delivery. A strong improvement mindset, with the motivation to challenge complexity and push through friction. Apply now for an confidential conversation

At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Credit Acceptance makes vehicle ownership possible for consumers other lenders decline, through a dealer network, an originations and servicing platform, and pricing and decisioning models refined over decades. The technology estate underneath this business was built project by project over a long period, and modernizing it is the largest engineering opportunity at the company. As the Distinguished Engineer, you are the senior-most individual contributor in Technology, reporting directly to the Chief Technology Officer. You will set the multi-year technical strategy for the company while staying close enough to the code and systems to remain credible - this is not an architect role that reviews designs from a distance, nor a strong engineer role that goes deep in a single domain. Two things sit at the center of the job: the target architecture for the enterprise, and the architecture for running AI agents across it. You will not own a team; you will own the hardest technical problems the company has, and get them resolved by convincing the people who do own the teams. Outcomes and Activities Enterprise target architecture: Convene the principal and staff engineers who own each domain to define the end-state architecture across originations, servicing, marketplace, data, and AI, and use it to drive the multi-year plan. Ensure every component has a defined interface with stated inputs and outputs, every unmade decision has a written principle standing in for it, and the document states honestly how close the organization is to the end state. Publish the architecture, including the tradeoffs that were rejected and why. Agentic AI architecture across the enterprise: Own the enterprise agent architecture spanning servicing and collections, the dealer side, originations, and internal engineering and back-office workflows, built on one common foundation rather than one stack per team. Establish a governed way for agents to discover and call business capabilities, data, and tools, using the Model Context Protocol as the integration layer. Define an identity and authorization model that treats an agent as a first-class actor with its own least-privilege entitlements, along with observability, evaluation, and cost controls that work consistently for every agent. Set policy on what an agent may decide independently versus what stays with a person, and define where that line moves and on what evidence. Design the architecture to produce an examiner-ready record of what each agent saw, recommended, and touched, and set the evaluation and adversarial testing approach that makes agent outcomes defensible. Ensure the same foundation and controls apply to agents built by outside firms on the company's behalf. Migration off on-premises infrastructure: Own the technical strategy for migrating transactional and analytical workloads off on-premises infrastructure to the company's strategic targets - AWS for transactional workloads and Databricks for analytical workloads - determining what lifts and shifts, what replatforms, what gets re-architected on the way versus after, and what the steady-state cost looks like. Own the resilience and recovery architecture for the target state. Data platform architecture: Set the architecture for change-data-capture-based ingestion, streaming where warranted, and a modern warehouse with no job-scheduling dependency, moving the organization from largely batch analytical data movement to real time. Help settle the open question of who owns the transactional and analytical data models. Real-time decisioning, model serving, and experimentation: Architect for pricing and credit model and scorecard updates that drop in as a layer rather than requiring a multi-quarter rebuild, and for an experimentation platform that supports orders of magnitude more concurrent experiments than a conventional release process allows. Marketplace and search architecture: Own the architecture and interfaces for a unified vehicle inventory, consumer search, and lead routing platform, ensuring the data model supports more than one class of seller from the start. AI-native engineering practice: Define the golden path for how software is specified, generated, reviewed, tested, and deployed, including the analyzers and gates that enforce it, and measure the inputs to that practice rather than just the outputs. Hold the build-versus-buy bar for AI capability, building what is core competency and about to scale, and renting the rest. Performance and peak readiness: Own the instrumentation, baseline, and remediation architecture needed to meet the standard that no page takes more than one second. Own peak-event headroom, including understanding the limits of each system and maintaining tested degradation levers ahead of need. Operating approach: Move the organization from project-shaped systems toward owned platforms with stated contracts. Document every component's inputs and outputs, and be ready to argue why closely coupled components should not be consolidated into one. Work toward a two-week requirement-to-production iteration bar, treating architecture that cannot be delivered incrementally as unfinished. Name what comes off the plan whenever a change in direction is requested. Make the call and own the outcome on architectural recommendations. Treat security, privacy, auditability, and regulatory obligation as design inputs from the start in this regulated environment. Hands-on, not hands-off: This is not optional. Stay personally engaged in the technical work rather than directing it from a distance. Join design reviews, write proofs of concept, read code, and engage in incident analysis. This hands-on time does not replace the work the teams own, and it is not a fallback when delivery slips. Every engineer in the organization will read how you write, how you argue a design, how you handle being wrong, and how you treat engineers who disagree with you as the bar. Staff and principal engineers should be measurably better at this work for having worked with you. Success measures: A published target architecture the domain owners agree with and that the annual and long-range plans are actually written against; an enterprise agent foundation that internal teams and partners build on, with access controls, evaluation, and audit records in place; a credible, dated, costed migration path off on-premises infrastructure and demonstrated progress against it; measured improvement in chosen input metrics such as latency, availability, recovery objectives, deployment frequency, and experiment throughput; growth in the number of principal and staff engineers who are better at this work as a result of this role; and architectural decisions that stop being relitigated because the reasoning was written down and the interfaces were clear. Competencies Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience. Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions. One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively. Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business. Requirements Bachelor's degree in Computer Science, Information Systems, or closely related field of study; or equivalent work experience Minimum 10 years of software engineering experience or comparable depth of experience A track record of setting and delivering multi-year technical strategy at enterprise scale, as an individual contributor rather than through a reporting line. Depth in distributed systems architecture, and in at least two of: agentic or LLM-based systems in production; large-scale cloud migration of transactional systems; modern data platform and lakehouse architecture; machine learning platform and model serving; high-traffic consumer or marketplace applications. Architecture ownership of agentic or LLM-based systems that reached production, including how tool and data access were governed, how the system was evaluated . click apply for full job details

Pay Range: $50.00hr - $55.00hr Job Overview: As a Consulting Data Engineer, the successful candidate will architect, build, and optimize the next generation of real-time healthcare data platforms and event-driven architectures. The candidate will play a foundational role in moving clinical, operational, and financial data away from legacy batch processing toward high-throughput, near-zero-latency streaming pipelines. In this role, the candidate will champion modern data engineering practices, design highly scalable cloud-native pipelines, and mentor a growing team of engineers. The candidate will bridge the gap between complex electronic health records (EHR) and data consumers-enabling data scientists, software engineers, and clinical analysts to securely access, stream, and query compliant data at scale to improve patient outcomes and operational efficiency. Core Responsibilities: Architect Real-Time Pipelines: Design, implement, and monitor high-throughput, fault-tolerant streaming pipelines that ingest and process millions of concurrent healthcare data events (e.g., live telemetry, HL7 feeds, ADT messages). Scale Google Cloud Infrastructure: Build and maintain scalable, secure, and cost-effective cloud data infrastructure using Google Cloud healthcare-native tools and Infrastructure as Code (IaC). Enforce Healthcare Compliance & Security: Uphold strict data security protocols (HIPAA, HITRUST, SOC2) by building robust data lakehouse architectures that enforce PHI/PII masking, role-based access control (RBAC), and encryption at rest and in transit. Enable Self-Service Data & Interoperability: Build out streaming data ecosystems that support modern healthcare interoperability, managing real-time data catalogs and schema registries. Promote Code Excellence: Write modular, highly reusable, and performance-tested code; lead code reviews and establish standardized CI/CD automated deployment workflows. Optimize Data Reliability: Set and champion strict SLAs for clinical data delivery, establish automated data-quality testing, and implement proactive system monitoring and alerting. Collaborate & Mentor: Work closely with Data Architects, Clinical Product Managers, and AI/ML teams to translate healthcare requirements into technical blueprints while mentoring mid-level engineers. Required Qualifications: 13+ years of experience in dedicated data engineering, data architecture, or distributed systems software engineering roles. 4+ years of experience specifically within the healthcare domain (payer, provider, digital health, or health tech). Google Cloud Certified Professional Data Engineer (or equivalent proven expert-level architecture experience on GCP). Advanced Event-Driven Architecture: Mastery of high-volume streaming technologies including Apache Kafka and Google Cloud Pub/Sub for decoupled, real-time message routing. Stream Processing: Deep hands-on experience with Apache Flink or Google Cloud Dataflow (Apache Beam) for real-time streaming, complex event processing (CEP), windowing, and streaming data transformations. Interoperability Standards: Deep expertise working with FHIR (Fast Healthcare Interoperability Resources) JSON structures, HL7 v2/v3 messaging, and DICOM imaging standard streams. GCP Healthcare Tools: Hands-on experience implementing and scaling the Google Cloud Healthcare API (specifically FHIR stores and HL7v2 stores). EHR Integration: Experience integrating streaming pipelines with major EHR systems (such as Epic, Cerner, or Meditech). Google Cloud Ecosystem: Technical mastery of BigQuery (including partitioning, clustering, and streaming inserts), Cloud Storage, Dataproc, and Cloud Composer (Apache Airflow). Programming Languages: Proficiency in Python, Scala, or Java, with a strong focus on writing optimized code for distributed computing platforms (like Spark or Beam). Modern Data Stack & Modeling: Mastery of modern analytics engineering frameworks (such as dbt), dimensional data modeling, and lakehouse architectures (e.g., Iceberg, Delta Lake). DevOps & Automation: Robust experience building automated CI/CD pipelines (GitHub Actions, GitLab CI) and managing infrastructure using Terraform. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.

Job Description About Carpe & the Role Carpe is an AI-native company building platforms and data products that help P&C insurance carriers make better decisions. At Carpe, we don't hand-write code anymore. We build with AI. As a Senior AI Software Engineer, you will turn business needs and prototypes into reliable production systems by directing frontier models and coding agents, shaping the environments they work in, and verifying what they deliver. AI is your primary way of building-not an autocomplete tool added to a traditional coding workflow. You bring extensive hands-on experience with frontier models, agents, and agent harnesses, plus the engineering judgment to take their work all the way through AWS deployment and production operation. You'll have a say in how we build. Our design and architecture patterns give you a solid starting point, not a set of limits. If a new approach or technology will better serve our customers, make the case. We care far more about how you think through tradeoffs than whether you've used every tool we use. Responsibilities Turn business needs into executable work. Partner with Product to define the problem, constraints, acceptance criteria, and customer outcome. Give agents the specifications, repository context, examples, and verification steps they need to deliver the right solution; resolve ambiguity before it becomes working software that solves the wrong problem. Architect systems and direct AI delivery. Break complex work into bounded tasks, choose interfaces and data flows, and direct agents through implementation, integration, and debugging. Use our approved patterns to address scale, latency, reliability, and data correctness across products. You own the architecture and outcome, even when AI produces the code. Build effective agent workflows and harnesses. Scope and steer bots through our team workflow, including Slack and Linear. Configure the context, tools, permissions, execution environments, and feedback loops around agents; manage handoffs, checkpoints, and recovery when they stall or go off course. Run a tight build-and-verify loop: the agent builds, you exercise the result in a live test environment, feed back precise findings, and repeat until the acceptance criteria are met. Choose models and approaches deliberately. Evaluate frontier models and coding agents on real engineering tasks, comparing output quality, reliability, context handling, latency, and cost. Match the model and harness to the work, and adapt as capabilities change. Let customer outcomes, not tool loyalty, guide decisions. Prove the result, not the agent's claim. Review architecture and generated changes at the level the risk demands; personally inspect sensitive changes. Use independent checks, representative data, automated tests, regression checks, and end-to-end behavior to catch plausible-but-wrong output. Where systems use models at runtime, evaluate their behavior and failure modes too. Ship and operate in AWS. Take AI-built software through cloud deployment and production support with QA and DevOps. Direct and verify infrastructure-as-code and CI/CD changes; understand access controls, secrets, networking, data storage, rollout, and rollback. Use logs, metrics, and traces to diagnose failures, verify recovery, and own incident follow-through-including failures introduced by agents. Make the next AI build better. Turn successful approaches and failures into reusable agent instructions, repository guidance, harness improvements, and verification routines. Keep architecture and operational context current so both people and agents can build on it. What we are looking for 7+ years of professional software engineering, delivering and supporting production systems (overall experience, not five years with today's AI tools). Extensive frontier-model experience. Substantial use of frontier models across multiple model families-for example, OpenAI, Anthropic Claude, and Google Gemini-in real software delivery. You can explain which models you choose for planning, implementation, debugging, and review; where they fail; and how you evaluate a new release. You actively experiment and clearly prefer building with AI over a hand-coding-first workflow. Extensive agent and harness experience . You have shipped meaningful work with coding agents and agent harnesses-not just chat prompts or autocomplete. Examples may include Claude Code, Codex, other agentic development environments, or custom harnesses. Extra credit for OpenClaw. You can configure repository instructions, context and tool access, isolated execution, task decomposition, verification, and recovery, and explain when orchestration helps versus adding overhead. End-to-end systems ownership. You have personally designed a complete system through professional or substantial independent work, with relevant professional experience around similar systems. Show how you directed AI from an initial specification through a working, deployed result, what you personally decided, and how you established that it worked. You don't need to have led a large-system initiative. Engineering depth behind the agents. Strong programming, debugging, architecture, and systems reasoning. You can read and interrogate generated code, trace data flows and service interactions, identify flawed assumptions, and steer a correction. We value your ability to make AI-built systems correct and maintainable-not the volume of code you type. AWS and cloud deployment experience. You have deployed and supported production software in AWS and understand the path from repository to running service: infrastructure as code, CI/CD, compute or containers, storage, networking, IAM, secrets, observability, and rollback. Explain a deployment or incident you owned and how you verified the outcome. Roll-up-your-sleeves, low-ego collaboration . Comfortable steering agents, investigating failures, explaining tradeoffs, and influencing without direct authority in a small. Share work and blockers openly, move across products, and help teammates improve their AI workflows while staying accountable for the customer outcome. Why Carpe? Your work shows up fast. At our size, what you build reaches customers quickly, and you quickly make an impact Growth: We have a clear growth framework with defined levels and competencies, so you always know what's expected and what "next" looks like. Collaboration: Work with teams in sales, marketing, engineering, product, and customer success. We Live our Values: Be Bold, Be Creative - Lead with Empathy - Act Like an Owner Flexible working hours and flexible vacation policy 100% Carpe-paid health, dental, and life insurance (Additional benefits at Employee cost) Full-time permanent positions are eligible for the Carpe Data Bonus Plan. 401k with company matching Location We are headquartered in Santa Barbara, CA. We work on a hybrid schedule: Tuesday/Wednesday/Thursday in the office. Salary Range $155,000 - 180,000. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity.

Pay Range: $50.00hr - $55.00hr Job Overview: As a Consulting Data Engineer, the successful candidate will architect, build, and optimize the next generation of real-time healthcare data platforms and event-driven architectures. The candidate will play a foundational role in moving clinical, operational, and financial data away from legacy batch processing toward high-throughput, near-zero-latency streaming pipelines. In this role, the candidate will champion modern data engineering practices, design highly scalable cloud-native pipelines, and mentor a growing team of engineers. The candidate will bridge the gap between complex electronic health records (EHR) and data consumers-enabling data scientists, software engineers, and clinical analysts to securely access, stream, and query compliant data at scale to improve patient outcomes and operational efficiency. Core Responsibilities: Architect Real-Time Pipelines: Design, implement, and monitor high-throughput, fault-tolerant streaming pipelines that ingest and process millions of concurrent healthcare data events (e.g., live telemetry, HL7 feeds, ADT messages). Scale Google Cloud Infrastructure: Build and maintain scalable, secure, and cost-effective cloud data infrastructure using Google Cloud healthcare-native tools and Infrastructure as Code (IaC). Enforce Healthcare Compliance & Security: Uphold strict data security protocols (HIPAA, HITRUST, SOC2) by building robust data lakehouse architectures that enforce PHI/PII masking, role-based access control (RBAC), and encryption at rest and in transit. Enable Self-Service Data & Interoperability: Build out streaming data ecosystems that support modern healthcare interoperability, managing real-time data catalogs and schema registries. Promote Code Excellence: Write modular, highly reusable, and performance-tested code; lead code reviews and establish standardized CI/CD automated deployment workflows. Optimize Data Reliability: Set and champion strict SLAs for clinical data delivery, establish automated data-quality testing, and implement proactive system monitoring and alerting. Collaborate & Mentor: Work closely with Data Architects, Clinical Product Managers, and AI/ML teams to translate healthcare requirements into technical blueprints while mentoring mid-level engineers. Required Qualifications: 13+ years of experience in dedicated data engineering, data architecture, or distributed systems software engineering roles. 4+ years of experience specifically within the healthcare domain (payer, provider, digital health, or health tech). Google Cloud Certified Professional Data Engineer (or equivalent proven expert-level architecture experience on GCP). Advanced Event-Driven Architecture: Mastery of high-volume streaming technologies including Apache Kafka and Google Cloud Pub/Sub for decoupled, real-time message routing. Stream Processing: Deep hands-on experience with Apache Flink or Google Cloud Dataflow (Apache Beam) for real-time streaming, complex event processing (CEP), windowing, and streaming data transformations. Interoperability Standards: Deep expertise working with FHIR (Fast Healthcare Interoperability Resources) JSON structures, HL7 v2/v3 messaging, and DICOM imaging standard streams. GCP Healthcare Tools: Hands-on experience implementing and scaling the Google Cloud Healthcare API (specifically FHIR stores and HL7v2 stores). EHR Integration: Experience integrating streaming pipelines with major EHR systems (such as Epic, Cerner, or Meditech). Google Cloud Ecosystem: Technical mastery of BigQuery (including partitioning, clustering, and streaming inserts), Cloud Storage, Dataproc, and Cloud Composer (Apache Airflow). Programming Languages: Proficiency in Python, Scala, or Java, with a strong focus on writing optimized code for distributed computing platforms (like Spark or Beam). Modern Data Stack & Modeling: Mastery of modern analytics engineering frameworks (such as dbt), dimensional data modeling, and lakehouse architectures (e.g., Iceberg, Delta Lake). DevOps & Automation: Robust experience building automated CI/CD pipelines (GitHub Actions, GitLab CI) and managing infrastructure using Terraform. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.

Job Description Job is onsite in Ashburn, Virginia The Senior Data Center Network Engineer is responsible for the hands-on configuration, implementation, troubleshooting, maintenance, and lifecycle support of enterprise routing and switching infrastructure within an onsite Internet Data Center environment. The position's primary responsibility is network engineering: maintaining reliable Layer 2 and Layer 3 connectivity, implementing approved network changes, resolving complex incidents, supporting upgrades and migrations, and protecting network availability and performance. The engineer must be comfortable working directly with production routers, switches, interfaces, routing protocols, VLANs, trunks, circuits, and related network services. The role operates and supports Data Center operations, including DCIM data accuracy, rack-level equipment installation, structured cabling, fiber and copper cross-connect coordination, carrier circuit turn-up, technical documentation, vendor coordination, and participation in scheduled maintenance windows. This is a senior, hands-on position-not a primarily architectural, project-management, or remote-monitoring role. Key Responsibilities 1. Routing and Switching Engineering - Primary Focus Configure, implement, operate, and troubleshoot enterprise routers and switches in a production IDC environment. Perform hands-on Layer 2 administration, including: VLAN creation and modification Access and trunk port configuration Switch-port activation and validation Spanning-tree and loop-prevention troubleshooting Link aggregation and redundant-link support Interface, transceiver, and physical-connectivity troubleshooting Perform hands-on Layer 3 administration, including: IP addressing and subnet implementation Static and dynamic routing BGP and OSPF configuration and troubleshooting Route-policy, redistribution, and path-selection troubleshooting Gateway, adjacency, reachability, and failover validation VRF and network-segmentation support, where applicable Analyze and resolve routing, switching, packet-loss, latency, reachability, convergence, interface, and circuit-related problems. Use command-line, monitoring, logging, and diagnostic tools to isolate faults across physical, data-link, network, and transport layers. Review existing configurations, identify operational risks or inconsistencies, and recommend practical corrective actions. Develop and execute pre-change checks, implementation steps, rollback procedures, and post-change validation. Support network moves, adds, changes, migrations, upgrades, hardware refreshes, and decommissioning activities. Install and validate software or firmware upgrades in accordance with approved change-management procedures. Monitor network availability, utilization, error conditions, and performance trends; proactively address conditions that could affect service. Support redundancy, resiliency, and failover testing for network paths and critical connectivity. Maintain current network diagrams, configuration records, interface assignments, IP-addressing information, and as-built documentation. 2. Incident, Problem, and Change Management Respond to network incidents and service degradations; perform technical triage, fault isolation, recovery, and escalation. Lead or meaningfully contribute to root-cause analysis for recurring or high-impact network issues. Document troubleshooting actions, technical findings, corrective measures, and final resolution details in the applicable ticketing system. Open, update, monitor, resolve, and close incident, problem, service-request, and change records. Prepare and execute network changes under established approval and change-control processes. Coordinate implementation schedules, technical dependencies, maintenance windows, validation activities, and rollback readiness. Participate in planned after-hours maintenance when changes cannot safely be performed during normal business hours. Work with internal engineering teams, operations personnel, service providers, carriers, and equipment vendors to resolve hardware, software, circuit, and interoperability issues. 3. Data Center and Physical Network Infrastructure Support onsite routers, switches, network-management appliances, and associated data-center connectivity. Install, rack, cable, power, label, and validate network equipment in accordance with approved designs and data-center procedures. Replace or upgrade line cards, supervisor modules, power supplies, optics, interface cards, and other field-replaceable network components. Support fiber and copper cross-connects between carrier demarcation points, meet-me rooms, patch panels, cabinets, and network devices. Coordinate circuit extensions, port assignments, optics, demarcation details, patching, and end-to-end connectivity testing. Verify cable type, source and destination, equipment port, labeling, signal condition, and physical path before service activation. Troubleshoot structured-cabling and patching issues and coordinate corrective work with IDC personnel or cabling vendors. Maintain orderly cable routing, grooming, labeling, and documentation consistent with IDC standards. Support equipment moves, adds, changes, rack relocations, hardware refreshes, and decommissioning activities. Coordinate onsite vendor access, equipment delivery, installation, replacement, return-material authorization, and technical service actions. 4. DCIM and Configuration Documentation Use the designated Data Center Infrastructure Management (DCIM) platform as an operational system of record-not solely as a reference tool. Create, validate, and update records for: Installed routers, switches, modules, and related network equipment Rack, cabinet, and rack-unit locations Asset identifiers and serial numbers Physical ports, patching, structured cabling, and cross-connects Equipment moves, additions, replacements, and removals Reconcile DCIM information with the physical environment and correct discrepancies. Update DCIM and related as-built records promptly following installations, changes, cable work, or decommissioning. Maintain network diagrams, rack elevations, cable schedules, port maps, circuit records, methods of procedure, test results, and operational runbooks. Ensure documentation accurately reflects the implemented production environment. 5. Operational Coordination and Support Work collaboratively with network, security, server, storage, application, facilities, carrier, and IDC operations teams. Communicate technical issues clearly to both technical and non-technical stakeholders. Provide status updates during incidents, maintenance events, circuit implementations, and infrastructure projects. Support technical planning and execution for network migrations, equipment refreshes, capacity changes, and data-center initiatives. Participate in an after-hours or on-call support rotation for critical incidents and scheduled maintenance, as required. Follow applicable security, access-control, safety, incident-management, change-management, and data-center operating procedures. Required Qualifications Minimum of 10 years of hands-on routing and switching experience in enterprise, service-provider, government, or similarly complex production networks. Demonstrated experience independently configuring, implementing, maintaining, and troubleshooting production routers and switches. Strong knowledge of: TCP/IP and IPv4 networking Layer 2 switching, VLANs, trunking, and redundancy Layer 3 routing and route selection BGP and OSPF Network segmentation and resilient network design Network monitoring and diagnostic techniques Experience planning and executing production network changes, maintenance activities, upgrades, migrations, and cutovers. Experience troubleshooting complex connectivity and performance issues across network devices, carrier circuits, and physical infrastructure. At least three contiguous years of hands-on experience working in an IDC or comparable data-center environment. Practical experience with data-center physical infrastructure, including equipment installation, rack-level work, fiber and copper cabling, patch panels, transceivers, and cross-connects. Working experience with a DCIM platform, including validating and maintaining equipment, rack, port, cable, or asset records. Experience with incident, problem, and change-management processes and associated ticket documentation. Ability to produce accurate network diagrams, configuration records, implementation plans, rollback procedures, and as-built documentation. Strong analytical, troubleshooting, written-communication, and stakeholder-coordination skills. Ability to work independently onsite while collaborating effectively with distributed engineering and operations teams. U.S. citizenship. Ability to obtain and maintain a Public Trust clearance. Ability and willingness to work onsite Monday through Friday in Ashburn, Virginia for the duration of the engagement. Ability to support scheduled evening, overnight . click apply for full job details

Job Description Senior Software Engineer, Backend - Booking Platform & APIs New York, NY We are representing a client that is hiring a Senior Software Engineer to lead the technical work and be the primary hands-on builder for our API. In your first year, multiple distribution partners and an authorized AI agent should complete real bookings through the same versioned API. You will evolve a production Rails and Postgres booking system used by millions of guests. The hard work is keeping prices and inventory correct, preventing overselling, surviving retries and partial failures, and reconciling systems when they disagree. Your ownership and team You will own the API's day-to-day technical roadmap and design decisions. In this hands-on individual contributor role, you will write production code, lead focused designs, guide migrations, and operate what you build. You will partner with our Staff Engineer on foundational domain and architecture choices. You will be the primary builder, but you will not work alone. Other backend engineers will contribute and build supply, PMS, and POS connectors using the patterns you establish. Product, Partnerships, and hotel operations will work with you from requirements through production launch. This is one of our highest-impact technical bets. What you build will determine how quickly Our client can enter new booking channels and categories. This role works from our NYC headquarters four days per week. The base salary range is $190,000-$210,000, plus equity, based on experience. What you'll do Build and launch the external Client API. Define stable, versioned contracts for live pricing, availability, holds, booking creation, changes, and cancellations. Make booking operations safe under real failures. Design idempotency, recovery, webhooks, and reconciliation for retries, timeouts, duplicates, and partial success. Evolve the core booking domain. Create clear models and invariants for products, schedules, prices, inventory, holds, carts, and bookings. Make the platform extensible. Prove that a new category such as events or food and beverage can use the shared booking model instead of requiring a separate system. Own the architecture for supply, PMS, and POS integrations. Create reusable connector patterns so other engineers can add partners without reinventing failure handling and reconciliation. Define the data contracts that keep inventory discoverable through OpenSearch. Search is supporting technology for this role, not its primary ownership area. Operate what you build. Set reliability and latency targets, and add the tests, metrics, dashboards, alerts, tracing, and runbooks needed for a critical booking path. Turn partner needs into durable platform capabilities, explain tradeoffs clearly, and raise the engineering bar through practical reviews and mentoring. How we ship We move quickly by building quality from the start. A change is done when it is tested, deployed, observable, and safe to roll back. We use AI tools aggressively, but engineers remain accountable for architecture, correctness, and production outcomes. Tech stack Backend: Ruby on Rails, Node.js Data: Postgres, MongoDB Search: OpenSearch APIs and integrations: REST, OpenAPI, webhooks Infrastructure and observability: AWS, Datadog Testing: RSpec, Playwright Who you are We encourage candidates to apply even if they do not match every line below. You have 6+ years of software engineering experience, or a track record of equivalent impact. You have designed transactional systems, built external APIs or integration platforms, and safely evolved data models in production. You understand relational modeling, concurrency, consistency, asynchronous workflows, and distributed failures. You turn complex business rules into clear boundaries, lifecycle states, contracts, and invariants. You can lead an important system as its primary builder while collaborating on foundational decisions. You care about correctness, testing, observability, and safe rollouts. You communicate directly, listen well, and improve the work of engineers around you. Ruby on Rails experience is useful, but not required. Experience with marketplaces, reservations, inventory-constrained commerce, public APIs, or third-party booking systems is also helpful. We care most about your backend judgment, track record, and ability to learn. What success looks like in 6-12 months Multiple distribution partners and an authorized AI agent complete real bookings through one reusable API. The full booking lifecycle meets agreed targets for correctness, success rate, latency, overselling, recovery, and reconciliation. Reusable contracts and connector patterns materially reduce the work required to onboard the next partner. The shared booking model supports at least one new category without a separate booking system. As the API becomes a company-wide platform, this role can grow toward Staff through demonstrated impact across the booking domain, integrations, and distribution. Benefits Health, dental, and vision coverage, with our client contributing to monthly premiums Stock options 401(k) plan Unlimited paid time off Paid parental leave

Job Description Cohu is a global supplier of semiconductor test and automation equipment. In this role you will build and sustain software that controls complex electro-mechanical test handlers used in high-volume semiconductor manufacturing. Position Summary Design and develop proprietary Windows desktop application and framework software for control machines, automation systems, and complex electro-mechanical platforms - including motion controllers, I/O systems, sensors, machine vision systems, robotics, and barcode readers. This role spans full lifecycle development-from architecture and design through integration, deployment, and customer support-focused on uptime, safety, and predictable behavior. This position is ideal for engineers who enjoy building large software systems that interact with sophisticated industrial equipment. Essential Functions / Responsibilities Design, develop, test, and integrate machine control software using C++ and C# in a Visual Studio environment. Develop automation software using object-oriented design principles and software design patterns. Integrate motion controllers, I/O systems, machine vision, robotics, sensors, barcode readers, and other industrial devices through vendor-provided Windows APIs and SDKs. Drive system architecture, interface definitions, and software design reviews; establish patterns that scale across product variants. Own customer escalation response: troubleshoot issues using logs, instrumentation, and on-equipment debugging; deliver corrective actions and prevention plans. Partner with Manufacturing and Field Service to resolve production-line and installed-base issues, restore system uptime, and improve reliability. Build verification strategies that push failure detection upstream (simulation, automated system tests, regression suites) and reduce re-occurrence. Interface with customers and cross-functional teams (EE/ME/Test/Apps) to align requirements, integration plans, and release readiness. Mentor engineers through code reviews, design feedback, and technical coaching. Required Qualifications Bachelor's or Master's degree in Computer Engineering, Software Engineering, Computer Science, Electrical Engineering, or a related field. 10+ years of professional software engineering experience, including substantial work on automation, machine control, or complex electro-mechanical systems. Expert C/C++ skills with object-oriented design (OOD) and design patterns; ability to write maintainable, testable, production-quality code. Strong experience developing on Windows desktop applications using Visual Studio; familiarity with C# for tooling, diagnostics, or UI is preferred. Experience developing multithreaded software and event-driven software. Experience integrating external hardware using Windows APIs, SDKs, or communication libraries. Proficiency with version control and collaborative development workflows (Git preferred): code reviews, branching strategies, and disciplined change management. Demonstrated ability to troubleshoot and resolve field issues, including root-cause analysis and implementing corrective/preventive actions. Strong written and verbal communication skills; comfortable presenting designs and status to technical and non-technical stakeholders. Relevant Domain Experience (Any of the Following) Semiconductor equipment, test handlers, ATE integration, or factory automation environments. Comparable experience in industrial automation, robotics, medical devices, or high-volume product engineering with electro-mechanical systems. Preferred / Additional Skills (Highly Beneficial for Test Handler Software) State machine development, event-driven software architecture, machine sequencing, hardware integration through vendor API, motion control APIs, machine vision integration. Hardware/software integration experience (MCU/SoC peripherals, serial buses, timing/latency considerations) and familiarity with common interfaces (RS-232/RS-485, USB, Ethernet, TCP/IP, CAN, I2C, SPI). UI development experience for operator/service tools (WPF, WinForms, or Qt); ability to build effective diagnostics and recovery workflows. Build and release tooling: CI/CD pipelines (e.g., Jenkins), automated test execution, packaging/deployment, and traceable release documentation. Experience with factory/equipment communication standards such as SECS/GEM and related SEMI standards (e.g., E5/E30/E37/HSMS) and/or equipment performance tracking. Experience with simulation or hardware abstraction techniques that enable development and testing before hardware availability. Scripting and data analysis for troubleshooting (e.g., Python) and familiarity with issue tracking (e.g., Jira). Understanding of machine safety concepts: interlocks, e-stops, safe-state design, and failure modes; exposure to functional safety standards (e.g., ISO 13849) is a plus. What Success Looks Like You deliver reliable software that maintains throughput and uptime in production environments. You design scalable software architectures used across multiple products. You reduce escalation frequency by improving diagnosability and recovery behavior. You improve development velocity through pragmatic architecture, automation, and strong engineering hygiene. You mentor engineers and elevate software engineering standards across the organization. You collaborate effectively across disciplines and communicate trade-offs clearly Job Conditions / Additional Information Occasional domestic and international travel may be required (approximately 10%). Work involves extended periods at a computer workstation and may include time in manufacturing areas or at customer sites when supporting escalations. Compensation: The estimated base pay range for this position is $113,000-170,000 per year, depending on qualifications, experience, education, and other job-related factors. This range represents Cohu's good faith estimate of what it reasonably expects to pay for this role upon hire. This role may also be eligible for other compensation programs as determined by company policy. Featured benefits Medical, dental & vision insurance 401(k) with company matching contributions Employee Stock Purchase Plan Tuition assistance Disability & life insurance Profit Sharing With more than 3000 employees worldwide, we offer challenging and rewarding work experiences, generous employee benefits and a strong company culture. If you are looking for a global publicly traded company that provides you with international experience and a challenging work environment, then Cohu is your choice. Connect with Cohu Connect with your future Cohu firmly supports the U.S. national and various state and local policies of equal employment opportunity which are designed to provide equality of employment and advancement opportunities to every individual without regard to unlawful considerations of race, color, religion, national origin, citizenship status, ancestry, gender, gender identity or gender expression, age, marital status, sexual orientation, disability, medical conditions, pregnancy, genetic information, military or veteran status or any other legally protected category. In addition, reasonable accommodations are available to qualified disabled individuals, upon request. Globally, Cohu is committed to full compliance with all applicable laws and regulations governing employment, in the U.S. and in all other locations around the world where we have operations.

Job Description Top tier financial investment firm seeks an AI Engineer who will lead the design, build, and end-to-end delivery of business-facing AI applications and automation across the Firm. As a member of the AI team within Business Technology, the AI Engineer will work directly with front office Investment Profesionals to understand transaction workflows, translate business needs into technical specifications, and own delivery of practical solutions within the Firm's ecosystem, anticipating issues and managing priorities, resources, and timelines. This is a hands-on, full-stack engineering role combining stakeholder engagement, requirements analysis, software development, testing, deployment, and support. The AI Engineer will also contribute to the Firm's internal AI platform by building and maintaining reusable API integrations, Model Context Protocol (MCP) connectors, and workflow components that securely connect AI applications and agents to approved enterprise systems and data. The AI Engineer will lead day-to-day delivery with a high degree of independence, provide technical guidance and mentoring to junior colleagues, and coordinate with Businesses, Data Science, IT, Information Security, Compliance, and external vendors. Key Responsibilities: Partner with Business to lead requirements gathering and documentation; translate workflows into user stories, technical specifications, and acceptance criteria. Evaluate existing tools before recommending a new build. Lead the design and development of full-stack applications, dashboards, and workflow tools, connecting user interfaces, APIs, databases, and approved AI services to support information synthesis and banker productivity. Own the design, build, testing, and maintenance of secure, reusable API integrations and MCP connectors between the Firm's AI platform and approved enterprise systems, data sources, and third-party services. Lead the development and maintenance of reviewed, configurable templates and reusable components so solutions can be adapted across transactions rather than rebuilt for each deal. Guide AI workflow orchestration and contribute to technical planning. Apply and reinforce engineering best practices, including version control, peer code review, automated testing, secure credential management, logging, and technical documentation. Evaluate AI output quality, reliability, latency, and cost. Lead user acceptance testing, user training, and feedback. Work with IT and engineering on approved deployment, monitoring, support handover, and retirement of temporary deal tools. Partner with Information Security and Compliance to identify and review material changes to solutions or enhancements that warrant additional assessment. Required Skills: Strong hands-on software development expertise in Python and JavaScript or TypeScript , with experience building web interfaces and backend services using frameworks such as React, Next.js, or Django. Demonstrated expertise designing, building, consuming, and troubleshooting APIs and connectors, with familiarity with Model Context Protocol (MCP) desirable. Strong working knowledge of SQL and relational databases Proficiency in Git, testing, debugging, and deployment practices, with the ability to write clear, maintainable, reviewable code. Working knowledge of generative AI architecture and demonstrated experience with large language model integration, including output validation and the limitations of AI-generated results and code. Ability to translate complex business needs into workable technical solutions, exercise sound technical judgment, and explain trade-offs and business objectives clearly to technical and non-technical stakeholders. Strong analytical thinking, organization, documentation, attention to detail, and customer service orientation. Sound judgment when handling confidential information and escalating risks. Intellectual curiosity, adaptability, and the ability to lead projects from start to finish with a high degree of independence, guide junior colleagues, and recognize when to involve senior colleagues on architecture, security, and production readiness. Plusses: Experience with AI-enabled applications, retrieval-augmented generation, agent workflows, or model evaluation. Experience with cloud platforms such as Azure, containers, CI/CD, authentication, role-based access controls, and application monitoring. Experience working in an Agile environment, using tools such as Jira, and conducting stakeholder demos, user acceptance testing, or user training. Education & Experience: Bachelor's degree in Computer Science, Software Engineering, or a related technical field Demonstrated professional experience delivering full-stack applications and integrations, with ownership of projects or workstreams from requirements through deployment and support.

Job Description Intellibus designs and engineers platforms trusted by some of the most established FinTech and financial trading firms in the world. Our engineering teams build systems that power high-volume trading, real-time financial data, and mission-critical infrastructure. We expect our engineers to operate at a level where technical depth, sound judgment, and clear communication all carry weight - the problems are complex, the systems are production-critical, and the people you work with will hold you to a high standard. The Staff Engineer Role We are hiring a Staff Engineer to design, build, and own full-stack systems powering financial platforms - from frontend architecture to backend services to distributed event-driven infrastructure. This is not a role where you pick up tickets and ship features. You will be expected to think end-to-end: to understand the business problem, design the system, make the trade-offs, and deliver production-grade software that performs reliably under heavy load. You will work directly with senior stakeholders across technical and non-technical functions, often navigating competing priorities, ambiguity, and high-pressure timelines. The ability to communicate clearly, manage complexity across people and systems, and maintain a high quality bar when things get difficult is essential. If you have built and shipped systems that mattered - at a startup, at a large company, or somewhere in between - and you are looking for a role where you will be challenged technically while also growing your ability to operate in complex, high-stakes environments, we want to hear from you. What You Will Do Frontend Architecture Build scalable frontend platforms using React, Next.js, and TypeScript. Design modern UI architectures: server-side rendering, component-driven design, performance optimization, and state management patterns. Develop reusable component systems and scalable frontend frameworks. Backend Engineering Build backend services using Node.js and NestJS. Design and implement REST APIs, microservices, asynchronous workflows, and high-performance data processing systems. Work with SQL and NoSQL databases using TypeORM, and optimize for throughput and reliability. Event-Driven & Distributed Systems Design distributed systems built on event-driven architecture using Kafka, Azure Service Bus, Azure Event Grid, and messaging/queue systems. Build services that process high-volume financial events and real-time trading data. Cloud & Infrastructure Deploy and operate systems on Microsoft Azure, including Azure App Services, Azure Data Fabric, messaging platforms, Docker, CI/CD pipelines, and Linux environments. Design infrastructure that supports resilient, scalable distributed systems. What Sets You Apart We are looking for engineers who demonstrate several of the following: You have designed and operated event-driven distributed systems that process high-volume real-time data. You have a deep understanding of algorithms, data structures, and time complexity - and you apply that understanding to build systems, not just pass interviews. You have built full-stack platforms using React/Next.js and Node.js/NestJS that run reliably in production. You have hands-on experience with message queues, streaming platforms, and asynchronous workflows - Kafka, Azure Service Bus, BullMQ, or similar. You reason clearly about system trade-offs: latency vs. throughput, consistency vs. availability, speed vs. reliability. You have operated systems in cloud environments like Microsoft Azure, including debugging production incidents and improving performance under pressure. You design systems end-to-end and take ownership of architecture and production outcomes - not just the code you wrote. You have mentored other engineers and contributed to architecture reviews, engineering standards, and technical decision-making. You communicate well under pressure. You can hold your ground in a room full of stakeholders, explain a technical decision to a non-technical audience, and navigate situations where the right path forward is not obvious. Technology Ecosystem Frontend: React, Next.js, TypeScript, JavaScript, TailwindCSS, HTML5, CSS3 Backend: Node.js, NestJS, REST APIs, Microservices, BullMQ, TypeORM Event-Driven: Kafka, Azure Service Bus, Azure Event Grid Infrastructure: Microsoft Azure, Azure Data Fabric, Docker, CI/CD, Infrastructure-as-Code, Linux Data: SQL, NoSQL, Azure Data Fabric Compensation: $75 - $85 / Hour Our Process 15-minute video call with someone from our team 4 Proctored GQ Tests (under 2 hours) 30-45-minute final video interview Job offer If this sounds like the kind of work you want to be doing, apply now. Our team will be in touch within the hour.