Note: The job is a remote job and is open to candidates in USA. Provectus is a Premier AWS partner and Anthropic Strategic Partner helping enterprises apply AI, agentic systems, and their data to achieve measurable business outcomes. The Principal Forward Deployed AI Engineer will embed with customers, learn and redesign their business functions, ship production GenAI systems, and own outcomes tied to business KPIs.
Responsibilities
- Do the operator’s job for two to four weeks at the start of an engagement. Learn the function from inside, not from a requirements doc
- Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow — in weeks, not quarters
- Sit with the operator and the Forward Deployed Executive and redesign the function from first principles. Discovery, user research, and PRD-writing collapse into one team that re-imagines its own job. You are all three roles
- Ship production GenAI systems into the customer’s environment — LLM applications, agentic workflows, retrieval and structured-extraction pipelines, and the services around them. Running software, not recommendations
- Build the evaluation harness before you build the feature. When the engagement is bound to a business KPI, “it looked good in testing” is not an answer. Define what working means, instrument it, and let the evals drive the design
- Write production code across the stack — backend services, data pipelines, and the AI layer. Python and TypeScript are our centre of gravity; we choose tools to fit the customer, not the résumé
- Take systems to production on AWS (GCP/Azure where the customer requires it): containerized, observable, and maintainable after we leave
- Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from
- Work in a pair with a Forward Deployed Executive who carries the Business Unit’s KPIs. Your work is measured against the same number
- Drive adoption. A system the BU routes around has not shipped. Change management is part of the engineering job here, not a phase after it
- Be credible with the customer’s engineers, their operators, and their executives — and be willing to disagree with all three
- Shape what we commit to before we commit to it. You’ll have the standing to do it, because you’re the one who will build it
Skills
- **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
- Strong hands-on prodcution experience with **Claude Code/Cowork.**
- 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
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
Company Overview
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