🕒 yesterday
Own the design and production delivery of enterprise AI systems, embedding with client teams to understand operational needs, ship GenAI solutions, and shape AI adoption from strategy through deployment.
Forward Deployed AI Architect (GenAI, AWS)
🕒 yesterday
Generative AILarge Language ModelsClaudeClaude Code
📜 Description
- Learn customer workflows and constraints from the operator’s seat, then rebuild functions from first principles alongside clients.
- Own autonomous delivery within customer teams, from framing the problem through production deployment.
- Start from proven industry Blueprints and feed field learnings back into them.
- Deliver Cowork Activation, Agentic SDLC, and Blueprint Activations for financial services and healthcare.
- Help enterprises adopt AI from strategy through first deployment and shape the practice’s tooling and frameworks.
🛠️ Requirements
- Typically 7+ years building software, including substantial accountable production coding; remain hands-on.
- Have shipped GenAI/LLM systems to production and built or owned evaluation suites for nondeterministic systems.
- Be able to learn unfamiliar business functions quickly and work effectively in ambiguity.
- Strong engineering fundamentals and proficiency in Python and/or TypeScript.
- Experience delivering cloud-native systems on AWS; GCP or Azure experience is a plus.
- Presales experience and credibility with senior business and technical stakeholders.
- Hands-on experience with Claude, Claude Code, and Cowork, including workflows, MCP, integrations, connectors, or custom harnesses.
- Nice to have: Kubernetes or ECS, production CI/CD, AWS CDK, CloudFormation, Terraform, open-source contributions, or public writing on applied AI.
✨ Benefits
- On site visits to client’s facilities and our office in NYC once a week
- Unlimited Vacation policy
- Vision
- Dental insurance
- 401(k) matching plan
- No Visa sponsorship
Full job description
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:
What we look for:
Nice to have:
What We Offer:
We’re real:
How we hire:
- Recruitment interview
- A short practical task (about 2 hours) OR a live engineering session.
For the practical task, pick one of 2 or 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. - 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.
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