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Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world. Job Description About us New Co is a new AI-native product organization within Capgemini Financial Services. We build products, not projects: software for insurance claims, payment operations, and health operations, sold to banks, insurers, and health plans. Three product lines run on one shared platform, built by a deliberately small, senior team. Our engineering model is agentic: engineers author the specifications, tooling, evaluation suites, and guardrails, and AI agents do most of the implementation. Humans own every consequential decision, and in our regulated domains some decisions are human-only by design. The role Three product lines, one platform. You will own the platform that Claims, Payments, and Health run on: the agentic AI floor (model gateway, agent runtime, evaluation infrastructure, guardrails) and the shared product services around it (case management and work queues, integration connectors, multi-tenancy, metering). You run the platform as a product whose customers are our product teams, and you are its first and most senior engineer-leader. Every hour of claims handling or payment processing our products automate rests on infrastructure your group builds. What you will own The strategic vision, roadmap, and end-to-end lifecycle of the platform: from the model gateway and agent runtime to shared workflow, tenancy, and metering services A competitive engineering strategy at the frontier: you track research and model releases as they land, decide what the platform adopts versus builds, and keep our capability curve ahead of what clients could assemble themselves The closed improvement loops: production signals and evaluation verdicts feed reinforcement learning and fine-tuning pipelines that produce our own LLMs and SLMs; product loops run automated end to end, with humans holding the gates Build-vs-buy decisions across open-source and commercial AI infrastructure, and the boundary between what the platform provides and what product lines build themselves A disciplined operating model: a published capacity split between product-team requests, platform quality, and strategic initiatives; services graduate to self-service only when they are ready Platform adoption outcomes: your group is measured by the delivery metrics of its consumers, not its own output Compliance posture of the platform in regulated environments: model risk documentation, audit trails, and responsible-AI practices that bank and insurer risk teams can examine; no AI capability ships ungoverned or unevaluated, including the models we train ourselves Hiring and growing the platform group, and the engineering standards it sets for the whole organization What you will need A track record leading platform or infrastructure teams that ran production systems for multiple product teams, with accountability for adoption, not just delivery Hands-on credibility in modern AI infrastructure: LLM inference and serving, model gateways, vector search, guardrails, and evaluation systems Frontier research fluency: you read post-training, reinforcement learning, and agentic-systems work as it lands and can turn it into engineering strategy; an engineer who reads research, not a researcher at engineering distance Cloud platform depth (AWS, Azure, or GCP) with Kubernetes and infrastructure-as-code at production scale Experience delivering in a regulated industry, ideally financial services, or demonstrable fluency in what model-risk and security review requires of a platform A platform-as-product mindset: you can talk about golden paths, voluntary adoption, and developer research as naturally as architecture Daily, hands-on use of AI coding assistants in your own work What sets you apart You have owned both an AI platform floor and shared business services (workflow, tenancy, billing/metering) in one charter You have taken a model through post-training (RLHF, RLAIF, fine-tuning, or distillation to smaller models) into production Published or open-source work in agent infrastructure or evaluation tooling Cost management (FinOps) experience for LLM workloads Financial services domain depth: you have shipped production systems for banks, insurers, or payment providers The reference stack The reference technology stack for this role is our supported paved road: self-hosted Lang Smith and Lang Graph Platform as the agent runtime and evaluation plane, model providers behind a swappable gateway seam, PostgreSQL with pg vector plus Click House and S3-compatible object storage as the data platform, Neo4j Enterprise as the semantic knowledge graph, an agent memory plane serving episodic and precedent memory over MCP, MCP-native connectors, Open Telemetry and Grafana for observability, all on CNCF-conformant Kubernetes with Helm and Argo CD, deployable to any hyper scaler or on-prem. A tool-for-tool match is not expected: analogous experience counts fully. If you have built and operated systems of this shape on comparable components (a different orchestration framework, graph engine, evaluation platform, or serving stack), you have what we are looking for. How we work Engineers write specs, harnesses, evals, and guardrails; AI agents execute the implementation loops. Review, not typing, is where engineering judgment goes. Three human gates govern everything we ship: spec approval, merge, and release. Regulated code paths (money movement, authentication, cryptography, secrets) are always human-owned. Small and senior by design. No separate QA function, no scrum masters; quality comes from evaluation gates and whole-team review rituals. Domain experts (claims practitioners, payment scheme experts, clinicians) are full-time members of the product teams you will serve. Success in year one The first product line ships to its first enterprise client on platform services it chose to use, with platform cost attributed per line Platform adoption is voluntary and measured; product teams' deployment frequency and change-failure rates improve after adoption Bank or insurer model-risk teams accept the platform's evidence pack on first review A written engineering strategy exists, is re-argued each quarter against frontier developments, and the first New Co-tuned model (LLM or SLM) serves production traffic behind evaluation gates The base compensation range for this role in the posted location is 141546 - 203155 Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law. The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction. These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity. It is not typical for candidates to be hired at or near the top of the posted compensation range. In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws. Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include: Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave Medical, dental, and vision coverage (or provincial healthcare coordination in Canada) Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada) Life and disability insurance Employee assistance programs Other benefits as provided by local policy and eligibility Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini's discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation. Disclaimers Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment . click apply for full job details

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology-and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are seeking a hardworking and technically skilled engineer to join our Physical AI Technical Marketing team, focusing on building world class technical materials for world models within various industries. In this role, you will promote adoption and understanding of NVIDIA's advanced AI models dedicated to Autonomous Vehicles. You will do this through technical marketing initiatives, educational programs, and developer engagement at global events. What you'll be doing: Design and post-train foundation models (LLMs, VLMs, VLAs and DiTs) for real world applications across industries with a bias towards AV. Develop and Deliver Technical Content: Create and present high-impact, performance benchmark based technical marketing materials to drive enterprise and developer audiences towards adoption of NVIDIA platforms. Core Content Development: Build whitepapers, technical guides and blogs, videos, demos and solution briefs driving through NVIDIA sales and marketing teams to our AV partners and customers. Work with teams in research, software, and products to bring world models from idea to deployment. Serve as a domain expert in the area of World Models for autonomous vehicles. Capture feedback and respond to technical queries. Demonstrate development and integration workflows and examples. Enable Sales & Partners: Use training and enablement materials for developer relations teams, partners, and customers. Prototype and iterate rapidly on experiments across powerful AI domains, including agentic systems, reinforcement learning, reasoning, and video generation in partnership with customer / partner teams. Advance Thought Leadership: Support industry-firsts, developer success stories, analyst briefings, and speaking engagements. What we need to see: A BS Degree in Computer Science or Engineering related field or equivalent experience accompanied by comparable experience in a technical marketing role 8+ years of experience in technical marketing, developer enablement, or a related technical field with a focus on foundation models in AV use and evangelism. Skilled at translating sophisticated AI concepts into clear, engaging messaging for both technical and business audiences. Strong communication and collaborator leadership skills in matrixed, multi-functional environments. Comfortable with Python and tools like Hugging Face, PyTorch, Docker, or similar for model evaluation, inference, and code-level artifact review (config files, training checkpoints). Develop and improve technical content for corporate and software developer communities previously. Experience running generative AV AI models - LLMs, diffusion models, or VLMs / VLAs - along with a working understanding of their inputs, outputs, failure modes, and performance bottlenecks. Ways to Stand Out From The Crowd: Hands-on experience developing and delivering workshops or certification programs at industry events. Proficiency in software development standard methodologies, such as integrating AI agents into workflows. Deep understanding of foundation models and GPU-accelerated deployments. Strong understanding of AI licensing, model/data provenance, and traceability systems is a plus. Technical proficiency with NVIDIA technologies (Omniverse, Cosmos, CUDA, TensorRT, Triton Inference Server, etc.). Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 148,000 USD - 235,750 USD for Level 4, and 184,000 USD - 287,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until September 26, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.