Tekfortune Inc.
South Dartmouth, Massachusetts
Job Description Job Description Job Title: FSO Engineer Location: Dartmouth, MA 02747 Mode : Contract (6 Months) - Onsite Rate - $32/hr. Responsibilities: Recommend Services and standards for supporting the End-User Compute and Field Services environment/End-Users Deploy and manage in-scope hardware and software Support End-User data backup, storage and recovery Services for all End-Users Provision and support remote site networks ( e.g. , Local-Area Network (LAN), Wide-Area Network (WAN) connection, etc .) and related operations (e.g., procure, design, build, systems monitoring, Incident diagnostics, troubleshooting, Resolution and escalation, security management, and capacity planning/analysis), as required to meet computing requirements Provide support for stand-alone Uninterruptible Power Supply (UPS) systems Provide server break/fix and Level 2 Support for equipment and system software as coordinated through the IT and Clinical Service Desk Services Provide break/fix Level 2 Support for in-scope hardware and software as coordinated through the IT and Clinical Service Desk Services. Provide remote and deskside support to authorized users as needed to fulfill Service requirements Provide Incident determination, root cause analysis and Resolution Manage and maintain inventory of in-scope software licenses Ensure software license compliance and reconcile software installations with license rights Provide support for financial audits, as requested Perform access control (file systems, disk space, etc .), as it relates for an authorized user Manage and maintain inventory of in-scope hardware, include asset tagging and recording Provide technical support for all hardware/equipment of the remote site computing infrastructure Establish procedures for procuring appropriate equipment sparing requirements and maintain spares inventory levels for all in-scope Hardware and Equipment to meet temporary capacity needs and SLRs Manage and maintain spare inventory for in-scope hardware devices, advising of inventory levels of in-scope hardware in accordance with Policies and Procedures Track requests for new devices, spare components or peripherals Fulfill requests for replacement devices, components or peripherals from the spares inventory Provide ad hoc fulfillment of approved new devices, components or peripherals Manage device life cycle for devices as part of ongoing refresh process Provide advice to other vendors as required, on: Support remote site infrastructure system software ( e.g ., operating systems, utilities as listed in the applicable SOW Schedules, etc. ) Implement and coordinate all changes to the remote site infrastructure, including those that may affect the Service Levels of any other Recurring Services SOWs and Third Parties Create, maintain and provide all appropriate project plans, project time and cost estimates, technical specifications, management documentation, and management reporting in a form/format that is acceptable Identify and implement opportunities to consolidate infrastructure, improve utilization, availability, and cost in conjunction Provide BYOD capabilities and support Support provided and standard-approved projectors that are not part of the managed teleconferencing or video conferencing services
Job Description Job Description Job Title: FSO Engineer Location: Dartmouth, MA 02747 Mode : Contract (6 Months) - Onsite Rate - $32/hr. Responsibilities: Recommend Services and standards for supporting the End-User Compute and Field Services environment/End-Users Deploy and manage in-scope hardware and software Support End-User data backup, storage and recovery Services for all End-Users Provision and support remote site networks ( e.g. , Local-Area Network (LAN), Wide-Area Network (WAN) connection, etc .) and related operations (e.g., procure, design, build, systems monitoring, Incident diagnostics, troubleshooting, Resolution and escalation, security management, and capacity planning/analysis), as required to meet computing requirements Provide support for stand-alone Uninterruptible Power Supply (UPS) systems Provide server break/fix and Level 2 Support for equipment and system software as coordinated through the IT and Clinical Service Desk Services Provide break/fix Level 2 Support for in-scope hardware and software as coordinated through the IT and Clinical Service Desk Services. Provide remote and deskside support to authorized users as needed to fulfill Service requirements Provide Incident determination, root cause analysis and Resolution Manage and maintain inventory of in-scope software licenses Ensure software license compliance and reconcile software installations with license rights Provide support for financial audits, as requested Perform access control (file systems, disk space, etc .), as it relates for an authorized user Manage and maintain inventory of in-scope hardware, include asset tagging and recording Provide technical support for all hardware/equipment of the remote site computing infrastructure Establish procedures for procuring appropriate equipment sparing requirements and maintain spares inventory levels for all in-scope Hardware and Equipment to meet temporary capacity needs and SLRs Manage and maintain spare inventory for in-scope hardware devices, advising of inventory levels of in-scope hardware in accordance with Policies and Procedures Track requests for new devices, spare components or peripherals Fulfill requests for replacement devices, components or peripherals from the spares inventory Provide ad hoc fulfillment of approved new devices, components or peripherals Manage device life cycle for devices as part of ongoing refresh process Provide advice to other vendors as required, on: Support remote site infrastructure system software ( e.g ., operating systems, utilities as listed in the applicable SOW Schedules, etc. ) Implement and coordinate all changes to the remote site infrastructure, including those that may affect the Service Levels of any other Recurring Services SOWs and Third Parties Create, maintain and provide all appropriate project plans, project time and cost estimates, technical specifications, management documentation, and management reporting in a form/format that is acceptable Identify and implement opportunities to consolidate infrastructure, improve utilization, availability, and cost in conjunction Provide BYOD capabilities and support Support provided and standard-approved projectors that are not part of the managed teleconferencing or video conferencing services
Tekfortune Inc.
Dallas, Texas
Job Description Job Description Tekfortune is a fast-growing consulting firm specialized in permanent, contract & project-based staffing services for world s leading organizations in a broad range of industries. In this quickly changing economic landscape, virtual recruiting and remote work are critical for the future of work. To support the active project demands and skills gaps, our staffing experts can help you find the best job for you. Position: Agentic QA Engineer Generative AI & Agentic Systems (Agent, Multi-Agent Testing) Location: Dallas, TX _ Onsite only Duration: Long term Mode of interview: One Virtual and One face to face Mode of Job: 5 Days onsite Summary We are seeking a hands-on AI Engineer to design and execute end-to-end testing strategies for agentic AI solutions, including multi-agent systems in production-grade environments. This role partners with the Agentic Operations Team to ensure resiliency, reliability, accuracy, latency, orchestration correctness, and scale. You will establish QA frameworks, build reusable test artifacts, drive macro-level validations across complex workflows, and lead the QA function for Agentic AI from Dev to Prod. Key Responsibilities Agentic & Multi-Agent Testing Reliability, Resiliency, and Latency Accuracy & Macro-Level Validations Scale & Orchestration Dev Prod Readiness Define and own the QA strategy for agentic/multi-agent AI systems across dev, staging, and prod. Mentor a team of QA engineers; establish testing standards, coding guidelines for test harnesses, and review practices. Partner with Agentic Operations, Data Science, MLOps, and Platform teams to embed QA in the SDLC and incident response. Design tests for agent orchestration, tool calling, planner-executor loops, and inter-agent coordination (e.g., task decomposition, handoff integrity, and convergence to goals). Validate state management, context windows, memory/knowledge stores, and prompt/graph correctness under varying conditions. Implement scenario fuzzing (e.g., adversarial inputs, prompt perturbations, tool latency spikes, degraded APIs). Create resilience testing suites: chaos experiments, failover, retries/backoff, circuit-breaking, and degraded mode behavior. Establish latency SLOs and measure end-to-end response times across orchestration layers (LLM calls, tool invocations, queues). Ensure reliability through soak tests, canary verifications, and automated rollbacks. Define ground-truth and reference pipelines for task accuracy (exact match, semantic similarity, factuality checks). Build macro validation frameworks that validate task outcomes across multi-step agent workflows (e.g., complex data pipelines, content generation verification agent loops). Instrument guardrail validations (toxicity, PII, hallucination, policy compliance). Design load/stress tests for multi-agent graphs under scale (concurrency, throughput, queue depth, backpressure). Validate orchestrator correctness (DAG execution, retries, branching, timeouts, compensation paths). Engineer reusable test artifacts (scenario configs, synthetic datasets, prompt libraries, agent graph fixtures, simulators). Integrate tests into CI/CD (pre-merge gates, nightly, canary) and production monitoring with alerting tied to KPIs. Define release criteria and run operational readiness (performance, security, compliance, cost/latency budgets). Required Qualifications 7 years in Software QA/Testing, with 2 years in AI/ML or LLM-based systems; hands-on experience testing agentic/multi-agent architectures. Strong programming skills in Python experience building test harnesses, simulators, and fixtures. Experience with LLM evaluation (exact/soft match, BLEU/ROUGE, BERTScore, semantic similarity via embeddings), guardrails, and prompt testing. Expertise in distributed systems testing latency profiling, resiliency patterns (circuit breakers, retries), chaos engineering, and message queues. Familiarity with orchestration frameworks (LangChain, LangGraph, LlamaIndex, DSPy, OpenAI Assistants/Actions, Azure OpenAI orchestration, or similar). Proficiency with CI/CD (GitHub Actions/Azure DevOps), observability (OpenTelemetry, Prometheus/Grafana, Datadog), and feature flags/canaries. Solid understanding of privacy/security/compliance in AI systems (PII handling, content policies, model safety). Excellent communication and leadership skills; proven ability to work cross-functionally with Ops, Data, and Engineering. Preferred Qualifications Experience with multi-agent simulators, agent graph testing, and tooling latency emulation. Knowledge of MLOps (model versioning, datasets, evaluation pipelines) and A/B experimentation for LLMs. Background in cloud (AWS), serverless, containerization, and event-driven architectures. Prior ownership of cost/latency/SLAs for AI workloads in production. For more information and other jobs available please contact our recruitment team at . To view all the jobs available in the USA and Asia please visit our website at
Job Description Job Description Tekfortune is a fast-growing consulting firm specialized in permanent, contract & project-based staffing services for world s leading organizations in a broad range of industries. In this quickly changing economic landscape, virtual recruiting and remote work are critical for the future of work. To support the active project demands and skills gaps, our staffing experts can help you find the best job for you. Position: Agentic QA Engineer Generative AI & Agentic Systems (Agent, Multi-Agent Testing) Location: Dallas, TX _ Onsite only Duration: Long term Mode of interview: One Virtual and One face to face Mode of Job: 5 Days onsite Summary We are seeking a hands-on AI Engineer to design and execute end-to-end testing strategies for agentic AI solutions, including multi-agent systems in production-grade environments. This role partners with the Agentic Operations Team to ensure resiliency, reliability, accuracy, latency, orchestration correctness, and scale. You will establish QA frameworks, build reusable test artifacts, drive macro-level validations across complex workflows, and lead the QA function for Agentic AI from Dev to Prod. Key Responsibilities Agentic & Multi-Agent Testing Reliability, Resiliency, and Latency Accuracy & Macro-Level Validations Scale & Orchestration Dev Prod Readiness Define and own the QA strategy for agentic/multi-agent AI systems across dev, staging, and prod. Mentor a team of QA engineers; establish testing standards, coding guidelines for test harnesses, and review practices. Partner with Agentic Operations, Data Science, MLOps, and Platform teams to embed QA in the SDLC and incident response. Design tests for agent orchestration, tool calling, planner-executor loops, and inter-agent coordination (e.g., task decomposition, handoff integrity, and convergence to goals). Validate state management, context windows, memory/knowledge stores, and prompt/graph correctness under varying conditions. Implement scenario fuzzing (e.g., adversarial inputs, prompt perturbations, tool latency spikes, degraded APIs). Create resilience testing suites: chaos experiments, failover, retries/backoff, circuit-breaking, and degraded mode behavior. Establish latency SLOs and measure end-to-end response times across orchestration layers (LLM calls, tool invocations, queues). Ensure reliability through soak tests, canary verifications, and automated rollbacks. Define ground-truth and reference pipelines for task accuracy (exact match, semantic similarity, factuality checks). Build macro validation frameworks that validate task outcomes across multi-step agent workflows (e.g., complex data pipelines, content generation verification agent loops). Instrument guardrail validations (toxicity, PII, hallucination, policy compliance). Design load/stress tests for multi-agent graphs under scale (concurrency, throughput, queue depth, backpressure). Validate orchestrator correctness (DAG execution, retries, branching, timeouts, compensation paths). Engineer reusable test artifacts (scenario configs, synthetic datasets, prompt libraries, agent graph fixtures, simulators). Integrate tests into CI/CD (pre-merge gates, nightly, canary) and production monitoring with alerting tied to KPIs. Define release criteria and run operational readiness (performance, security, compliance, cost/latency budgets). Required Qualifications 7 years in Software QA/Testing, with 2 years in AI/ML or LLM-based systems; hands-on experience testing agentic/multi-agent architectures. Strong programming skills in Python experience building test harnesses, simulators, and fixtures. Experience with LLM evaluation (exact/soft match, BLEU/ROUGE, BERTScore, semantic similarity via embeddings), guardrails, and prompt testing. Expertise in distributed systems testing latency profiling, resiliency patterns (circuit breakers, retries), chaos engineering, and message queues. Familiarity with orchestration frameworks (LangChain, LangGraph, LlamaIndex, DSPy, OpenAI Assistants/Actions, Azure OpenAI orchestration, or similar). Proficiency with CI/CD (GitHub Actions/Azure DevOps), observability (OpenTelemetry, Prometheus/Grafana, Datadog), and feature flags/canaries. Solid understanding of privacy/security/compliance in AI systems (PII handling, content policies, model safety). Excellent communication and leadership skills; proven ability to work cross-functionally with Ops, Data, and Engineering. Preferred Qualifications Experience with multi-agent simulators, agent graph testing, and tooling latency emulation. Knowledge of MLOps (model versioning, datasets, evaluation pipelines) and A/B experimentation for LLMs. Background in cloud (AWS), serverless, containerization, and event-driven architectures. Prior ownership of cost/latency/SLAs for AI workloads in production. For more information and other jobs available please contact our recruitment team at . To view all the jobs available in the USA and Asia please visit our website at