AI Architect

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.

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 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 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 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

How we hire

1. Recruitment interview.
2. Tech interview with our FDE (30 min).
3. 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.
4. Soft skills interview.
5. Offer discussion.