• Lead the technical engagement with a focused set of ISV partners, working closely with their Software Platform Architects. • Together you'll set the objectives, timelines, and adoption plan for each account. • Stay hands-on: design and ship the code, methods, and reference architectures that bring RAG, inference, and multi-agent, long-horizon workflows to life on our stack (NeMo, Nemotron, NeMo Agent Toolkit, NIM, Dynamo, TensorRT-LLM) and open tools (vLLM, LangChain, vector DBs, MCP, A2A). • Lead the technical strategy and execution: run the cadence, track adoption, and share what you learn with our Product, Engineering, Research, and Solution Architecture teams to land the best solution. • Build breadth across the agentic AI lifecycle, with depth in a few areas that fit you: fine-tuning (PEFT, SFT), post-training and RL from verifiable rewards, reasoning, advanced RAG, multi-agent workflows, skills/harness engineering, agent evaluation and observability, and production inference. • Be the voice of the partner inside NVIDIA. Bring their needs and architecture to our Product, Engineering, and Research teams, and help shape the roadmap with what you see across industries. • Grow deep expertise in agentic platform architecture, and keep up with our fast-moving field and stack so you can give partners the best guidance.

• Own the delivery of complex mobile features end to end • Build and ship React Native features for iOS and Android • Contribute to complex mobile applications, including integration points, scalability concerns, and structural improvements • Work on code reviews and actively raise the quality bar across the team • Integrate AI tools (Copilot, ChatGPT) into the engineering workflow • Contribute to Angular front-ends or Node.js/NestJS/Next.js back-ends • Design and build APIs with a solid grasp of REST conventions • Break down complex technical problems and make recommendations • Mentor other developers and contribute to continuous improvement in culture

Role Description We’re building a new product engineering team to create AI-powered platforms from scratch. Our AI Personal Lines team has already shown what’s possible, standing up cloud infrastructure and delivering a production-ready application in a matter of weeks. We’re hiring for a senior full stack engineer to help scale that approach. You’ll work across the stack, integrate AI and third-party services, and ship in a secure cloud environment. You’ll join a small, high-trust team where speed, ownership, and delivery matter. Duties and Responsibilities: - Build full-stack features across a TypeScript and Next.js front end and a cloud-based back end, using a server-side AI proxy approach to keep API calls and secrets out of the browser. - Integrate AI and LLM capabilities, including OpenAI APIs, retrieval-augmented generation, conversational flows, and agent frameworks like LangGraph, along with GPT actions and MCP servers. - Stand up and connect cloud infrastructure on Azure, including Functions (Python), Service Bus, Storage, API Management, Front Door, and B2C authentication. - Build and maintain integrations with external APIs for things like quoting, identity, document retrieval, and third-party data. Handle authentication, validation, and the edge cases that come with real-world usage. - Take ownership of code as it moves from sandbox to dev to staging to production, using CI/CD, clear branching practices, automated tests, and quality checks. - Partner with security to support a hardened architecture, including penetration testing and compliance requirements. - Work in a fast-paced, AI-supported development workflow with daily standups and Jira tracking, and troubleshoot production issues as they come up. Qualifications - 5+ years of experience building and shipping production web applications as a full stack engineer. - Strong TypeScript and JavaScript skills with a modern React framework, ideally Next.js, along with working experience in Python. - Hands-on cloud experience, preferably in Azure including Functions, Service Bus, Storage, API Management, Front Door, and B2C. Strong AWS or GCP experience is also a good fit. - Experience integrating third-party REST APIs, including authentication, rate limiting, and handling real-world, inconsistent payloads. - Security-minded engineering habits, including proper handling of secrets, use of request proxying, and a clear understanding of network boundaries. - Comfort working with CI/CD, Git-based workflows, and promoting code through multiple environments. - Proven ability to move quickly and deliver in ambiguous, fast-changing environments as part of a small team. Requirements - Nice to Have: - Insurance, fintech, or other regulated-industry experience. - Infrastructure-as-code and cloud-architecture depth. - Experience integrating many external vendors or partners in parallel under time pressure. Benefits - Medical, Dental, Vision - Life and AD&D insurance - FSA / HSA - Commuter & Child Care FSA - Cancer Support Benefits - Pet Insurance - Accident & Critical Illness - Hospital Indemnity - Employee Assistance Program (EAP) - 11 Paid Holidays - Flexible PTO - 401K

• Lead the design and implementation of complex ML/AI systems end-to-end, owning architecture decisions and driving solutions from research through production at scale • Build and evolve scalable ML platforms, pipelines, and infrastructure that support reliable, repeatable model development and deployment across teams • Set the standard for AI-forward engineering, using tools like Claude and Cursor with sophistication and helping the team adopt them effectively • Partner with product, engineering, and leadership to shape AI strategy and align technical direction with business outcomes • Translate complex AI tradeoffs, risks, and opportunities into clear narratives that drive decision-making across technical and non-technical stakeholders • Lead design reviews and technical discussions, raising the bar for engineering rigor and constructive challenge across the team • Define AI architecture and engineering standards, bringing depth on tradeoffs, long-term implications, and responsible AI practices • Mentor and grow junior and mid-level engineers, multiplying impact through coaching, code reviews, and pairing on hard problems • Take ownership of the most ambiguous and highest-stakes pieces of work, driving them through to production with care for reliability, cost, and safety.