Provectus - Forward Deployed AI Engineer (GenAI, AWS)
Requirements
• 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. • 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. • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Responsibilities
• 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.
Benefits
• Frontier delivery work across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare • The chance to shape how leading enterprises adopt AI, from strategy through first deployment • A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers • A growing AI delivery practice where you help build the tooling and frameworks, not just use them • Remote-friendly culture • Short loop, hands-on, no take-home:
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