Senior Forward Deployed AI Engineer (GenAI, AWS)

Team: SA practice - Pavel Borobov

Location: New Jersey, New York, Pennsylvania, North Carolina, Connecticut, Massachusetts, Virginia

Commitment: Full-time

Workplace Type: remote

About the role:

At Provectus, you don’t prototype in a sandbox and hand a client a slide deck. You embed inside a bank, an insurer, or a health system, learn the work, and ship a Claude + AWS system into their real, regulated production — then you watch the business number move. We’re an Anthropic and AWS Partner with 150+ certified architects. That means the newest models, real budgets, and problems that matter — without the layers, politics, and years-to-impact of a hundred-thousand-person firm. 350+ builders, 100+ customers in production, founder-led since 2010, offices across North America, LATAM, and EMEA. 

Why Provectus

Frontier access — Anthropic Preferred Partner: newest Claude models, real budgets, real problems.

Real tasks (not scope), real deployment. You go where the operating problem is and ship production code to real users, not slide decks that sit in a drive. 

Embedded, not engaged. You are part of the customer’s team and inside their process — not a vendor running a project alongside it. Embedded is not staff-augmented.

Autonomous. You own the work from framing through to production, visible to clients and founders. You own the method; nobody hands you a ticket.

Your experience stays portable. Every engagement is a new problem, often in a new industry, so your skills compound instead of narrowing to one product. No legacy-maintenance grind, not a single stack.

Craft & growth — build beside senior FDEs on a Claude-certified team; a real platform (talks, OSS) to grow your name.

We give you a stage: Claude certification, conference talks, open-source contributions, published work under your own name.

Requirements:

• Typically 7+ 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.
• Genuinely willing to spend time understanding someone else’s job before writing code; demonstrated ability to become conversant in an unfamiliar business function quickly.
• 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), including the operational reality of a system someone else inherits.
• Pre-sales experience. 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.
• Hands-on experience with Claude, Claude Code, and Cowork (skills, workflows, MCP, integrations, connectors, custom harnesses).

Nice to have

• Containers and orchestration (Kubernetes/ECS) and CI/CD in production.
• IaC depth: AWS CDK, CloudFormation, Terraform.
• Open-source contributions or public writing on applied AI.

What you’ll do:

• Spend the first weeks of each engagement in the operator’s seat, learning the constraints from the inside, then rebuild the function from first principles alongside the client and a Forward Deployed Executive — and you are the one who builds it.

• Be part of the customer’s team, inside their process; work on real tasks, not scoped deliverables, and own the method autonomously.

• 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.

• Deliver across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare.

• Help shape how leading enterprises adopt AI, from strategy through first deployment; help build the tooling and frameworks of a growing AI delivery practice.

What We Offer:

• Full-time.

• On-site visits to client’s facilities and our office in NYC 1/week

• Unlimited Vacation policy.

• Generous health, vision, and dental insurance.

• 401(K) matching plan.

• Base salary $180k–$200k + bonuses; OTE up to $250k. 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.

• No Visa sponsorship

We’re real

• A person reads your application. We filter out AI-generated and fraudulent submissions automatically. Every application that clears that filter is read by someone on our team.

• We may ask for a quick ID verification. Fraudulent applications are common, and this protects real candidates.

• You’ll meet the people you’d work with. We host AI engineering events in New York and San Francisco. We'd be happy to talk in person. Your interviews are with Pavel Borobov, Solutions Architect, and Stepan Pushkarev, Co-Founder & CEO.

How we hire:

1. Recruitment interview.

2. A short practical task (about 2 hours) OR a live engineering session. 

• For the practical task, pick one of 2–3 use cases from our roster. They are synthetic problems, not a live client deliverable. 

• For the Live session, we hand you a business function and the constraints. You have to understand the job and rebuild it — then say what you’d build and how you’d know it worked. 

Use AI the way you normally would; we expect AI-generated code. What we look at is how you used AI, the judgment behind your choices, and what you researched beyond what any LLM already knows.

1. A task discussion session after the practical task. A discussion about what you’d build and how you’d know it worked. No LLMs for this one.

Why we ask for a task. We’re not using your work. We’re growing, and we need to find the right fit. We ask for a small investment so you can show your engineering and AI maturity — and that you’re a person, not a bot.