Provectus - Senior Forward Deployed AI Engineer (GenAI, AWS)

• 8+ years building software, a substantial share of it writing production code you were accountable for. You are hands-on today and intend to stay that way • You will take the operator’s seat. You are genuinely willing to spend weeks doing someone else’s job — claims processing, underwriting, revenue-cycle work — before you write a line of code. Engineers who need to stay in the IDE should not apply • You learn domains fast. Demonstrated ability to become conversant in an unfamiliar business function quickly enough to argue with the people who do it for a living • Shipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype works • You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and why • Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack • Cloud-native delivery on AWS (GCP/Azure a plus): containers, Kubernetes/ECS, IaC, CI/CD, and the operational reality of a system someone else inherits • Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either room • Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job • Solid AI/ML foundations — you understand what the models do well enough to reason about failure modes, not just call the API • Fluent English, written and spoken • Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution • Real depth in one of our blueprint industries: financial services, insurance, healthcare, asset management • Consulting, professional services, or other embedded customer-facing delivery • Data platform depth: data lakes, warehouses, streaming and real-time analytics, data mesh and data contracts, governance and data quality • MLOps and classical ML: PyTorch, SageMaker, MLflow • Fine-tuning, distillation, or inference/serving optimization • Graph databases (Neo4j, AWS Neptune) • IaC depth: AWS CDK, CloudFormation, Terraform • Open-source contributions or public writing on applied AI • Short loop, hands-on, no take-home: • Two live engineering sessions. Real problems, your own editor. You may use an LLM assistant (ChatGPT, Claude) — how you work now includes these tools. Autocomplete/agentic coding tools are off for these sessions • The redesign session. We hand you an unfamiliar business function and the constraints of the person who performs it. You have to understand the job well enough to rebuild it — then say what you’d build and how you’d know it worked. No LLMs for this one • Team and practice conversation