Forward Deployed AI Architect (GenAI, AWS)

About the role: Provectus is a Premier AWS partner and an Anthropic Strategic Partner at the forefront of applied AI, helping enterprises turn Claude, agentic systems, and their own data into measurable business outcomes through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide and are obsessed with reimagining how they operate and compete. Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. We embed engineers and leaders inside client operations as Forward Deployed Engineers (FDE) and Forward Deployed Executives (FDX) — people who learn the work, ship the system, and own the outcome. Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Bluprints. You will do the customer’s job before you automate it. Most AI engagements fail the same way: someone gathers requirements, someone writes a PRD, and a team ships a workflow nobody uses. We think the requirements-gathering step is the bug. So we remove it. A Forward Deployed AI Engineer at Provectus spends the first weeks of an engagement in the operator’s seat — as the underwriter, the analyst, the RCM specialist, the claims clinician, whoever actually does the work we’ve been asked to change. You do the job. You learn the constraints from the inside, the ones nobody writes down. Then you sit at a table with that operator and a Forward Deployed Executive and rebuild the function from first principles — and you are the one who builds it. Three things define how you work: Embedded, not engaged. You are part of the customer’s team and inside their process — not a vendor running a project alongside itReal tasks, not scope. You are not fenced into a siloed deliverable. You go where the operating problem isAutonomous. Embedded is not staff-augmented. You own the method; nobody hands you a ticket You won’t start from zero. Provectus builds industry blueprints — working systems that have already shipped for a customer in your industry. Your engagement starts from that baseline, and what you learn in the field goes back into it. That loop is the difference between an outcome and an invoice. You’ll be measured on whether the Business Unit’s number moved — not on hours, not on scope delivered. This is a role for engineers who have led before — as a founder, a CTO, a staff engineer — and who want to stay in the code while owning the outcome. On most days you’ll be the most senior technical person in the room, and you’ll still be the one shipping. Requirements: 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 wayYou 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 applyYou 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 livingShipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype worksYou evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and whyStrong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stackCloud-native delivery on AWS (GCP/Azure a plus): containers, Kubernetes/ECS, IaC, CI/CD, and the operational reality of a system someone else inheritsCredible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either roomComfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the jobSolid AI/ML foundations — you understand what the models do well enough to reason about failure modes, not just call the APIStrong hands-on prodcution experience with Claude Code/CoworkFluent English, written and spoken Nice to have: Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contributionReal depth in one of our blueprint industries: financial services, insurance, healthcare, asset managementConsulting, professional services, or other embedded customer-facing deliveryData platform depth: data lakes, warehouses, streaming and real-time analytics, data mesh and data contracts, governance and data qualityMLOps and classical ML: PyTorch, SageMaker, MLflowFine-tuning, distillation, or inference/serving optimizationGraph databases (Neo4j, AWS Neptune)IaC depth: AWS CDK, CloudFormation, TerraformOpen-source contributions or public writing on applied AI What you’ll do: Take the seat 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 docReach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow — in weeks, not quartersSit 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 Build 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 recommendationsBuild 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 designWrite 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 résuméTake systems to production on AWS (GCP/Azure where the customer requires it): containerized, observable, and maintainable after we leaveStart from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from Own the outcome Work in a pair with a Forward Deployed Executive who carries the Business Unit’s KPIs. Your work is measured against the same numberDrive adoption. A system the BU routes around has not shipped. Change management is part of the engineering job here, not a phase after itBe credible with the customer’s engineers, their operators, and their executives — and be willing to disagree with all threeShape 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 What We Offer: Frontier delivery work across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and HealthcareThe chance to shape how leading enterprises adopt AI, from strategy through first deploymentA forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed EngineersA growing AI delivery practice where you help build the tooling and frameworks, not just use themRemote-friendly cultureHigh-impact role with direct visibility to leadershipStrong earning potential with performance-based bonusesOpportunity to work with cutting-edge AI and cloud solutionsB2B contract model or full-time modelUnlimited Vacation policyGenerous health, vision, and dental insurance401(K) matching planOTE range $150-180k. The salary range is determined through interviews and a review of the education, experience, knowledge, skills, abilities of the applicant, and alignment with market data How we hire: Intro conversation — the role, your background, what you want to be doingTwo 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 sessionsThe 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 oneTeam 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. The salary range is determined through interviews and a review of the education, experience, knowledge, skills, and abilities of the applicant, and alignment with market data.