Senior Forward Deployed Engineer (GenAI, AWS)

Provectus
New York, NY
Hybrid

Who this role is best for

Strong fit for engineers with hands-on GenAI/LLM production experience who thrive in embedded, autonomous roles. Must have deep technical knowledge of Claude, AWS, and a history of real-world deployments in regulated industries.

Best fit for

  • Candidates with 7+ years of production software experience and a track record of deploying GenAI systems in regulated sectors
    — “7+ years building software, a substantial share of it writing production code you were accountable for”
  • Individuals who can quickly learn unfamiliar business functions and work directly with clients in embedded roles
    — “Genuinely willing to spend time understanding someone else’s job before writing code”
  • Engineers who value autonomy and ownership over deliverables, with a preference for real impact over theoretical work
    — “You own the work from framing through to production, visible to clients and founders”

Things to consider

  • No visa sponsorship — candidate must be eligible to work in the US without sponsorship
    — “No Visa sponsorship”
  • Commitment to on-site client work and travel, with weekly in-person meetings in NYC
    — “On site visits to client’s facilities and our office in NYC once a week”

How to stand out

  • Highlight deployments of GenAI/LLM systems in production, not just prototypes or demos
    — “Shipped GenAI/LLM systems to production, not demos, not notebooks”
  • Demonstrate experience with Claude, Claude Code, and Cowork in your technical narrative
    — “Hands on experience with Claude, Claude Code, and Cowork”
  • Showcase your ability to evaluate and measure performance for non-deterministic systems
    — “You have built or owned an eval suite for a nondeterministic system”
  • Emphasize your ability to work autonomously and take ownership of complex, underspecified problems
    — “Comfort with ambiguity and ownership. Engagements start underspecified by design”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • shipped GenAI/LLM systems to production
  • helped shape enterprise AI adoption
  • contributed to AI delivery practice
Typical background
software engineeringcloud computing

Skills & requirements

Required

GenaiAWSForward Deployed EngineeringProduction CodeCloud Native Delivery

Preferred

Anthropic Preferred PartnerClaude Certification

Stack & domain

GenaiAWSProduction CodeEmbeddedAutonomousMethodIndustry BlueprintsCowork ActivationAgentic SDLCBlueprint ActivationsFinancial ServicesHealthcareAI Delivery PracticePythonTypeScriptCloud Native DeliveryGCPAzurePresales ExperienceSenior StakeholdersLeadershipProblem-solvingTeamworkCommunicationCollaborationDecision-makingProject ManagementTechnical ExpertiseCreativityInnovationAI

About the role

Original posting from Provectus via Lever

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 long wait for impact of a firm with a hundred thousand people. 350+ builders, 100+ customers in production, led by our founders 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 augmentation.

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, with 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.

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. 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 look for::

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 nondeterministic 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

Presales 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 We Offer::

Full time

On site visits to client’s facilities and our office in NYC once a week

Unlimited Vacation policy

Generous health, vision, and dental insurance

401(k) matching plan

Base salary $180k to $250k + bonuses; OTE up to $300k. 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, Cofounder & CEO

How we hire::

  • Recruitment interview.
  • Tech interview with our FDE (30 min).

A live engineering building session (1.5 hours). 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.

  • Soft skills interview.
  • Offer discussion.

Source: Provectus careers (Lever)

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