Provectus is a Premier AWS partner and an Anthropic Preferred Partner at the forefront of applied AI, helping enterprises turn Claude, agentic systems, and their own data into measurable business outcomes.
With offices in North America, LATAM, and EMEA, we partner with clients worldwide. 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 rebuilds a critical business process front to back, shipped from working code. You'll learn the customer's job before you automate it. Most AI projects fail the same way: someone gathers requirements, someone writes a spec, and a team ships a workflow nobody uses. We think the requirements step is the bug. So we remove it.
As an Associate Forward Deployed Engineer, you'll join each engagement paired with a Senior FDE. Together you'll spend time alongside the client to understand their workflow and business needs. Then you'll help rebuild that work from first principles, and write real production code for it. You won't start from zero: every engagement begins from a Blueprint that has already shipped for a customer in that industry, and what you learn in the field goes back into it.
This role is for early-career engineers who want to learn how AI gets deployed in the real world — and who'd rather learn by building than by watching.
What You'll Need:
A degree in computer science, engineering or another STEM field
0–2 years of experience; internships, co-ops and research
You built projects, where you integrated AI as a part of your SDLC (internship, research or hackathon entry) that you can walk us through in depth: what worked, what broke, and what you'd change
Hands-on Claude Code basics
Strong programming fundamentals in Python or TypeScript
Curiosity and speed of learning — you pick up unfamiliar subjects quickly and ask good questions, especially about how real businesses work
Good commication skills - ability to explain complex technical subjects to non-engineers
Willingness to spend real time learning a client's job before you write code. It's the most unusual part of this role, and the most valuable
Location & Work Arrangement: This role requires working on-site from our New York office 3 days per week
Great If You Have:
Cloud basics: AWS/Azure/GCP, containers, CI/CD AI/ML coursework or foundations
Comfort with ambiguity — our engagements start open-ended by design Interest or coursework in financial services, insurance, healthcare or asset management
Data pipelines or SQL; classical ML or MLOps (PyTorch, SageMaker, MLflow); infrastructure-as-code
Open-source contributions, hackathon projects or public writing on applied AI
Claude certification or AWS certification
What You'll Learn:
How to deploy LLM and agentic systems into regulated production — banks, insurers, health systems
How to build evaluation harnesses: defining what "working" means, then measuring it
Cloud delivery on AWS Claude Code in production, working toward Claude certification
A real business domain — insurance underwriting, healthcare revenue cycle, asset management
How to work with clients: their operators, engineers and executives
Your first 90 days:
Foundations — onboarding into our Blueprints, our delivery stack (Claude Code, AWS) and how we build evals, plus a primer on your first client's industry
The seat — join an engagement with your senior FDE. Shadow the operator, then do the work yourself
Your first piece — own a scoped part of a production system, with code review and your senior FDE beside you
What You'll Do
Take the seat — learn a client's function from the inside, and help redesign it alongside your senior FDE and the Forward Deployed Executive
Build — ship production code for LLM applications, agentic workflows, and retrieval and extraction pipelines
Build the evaluation harness before the feature — define what working means, then measure it
Take systems to production on AWS, and feed what you learn back into the Blueprint
Grow into ownership — your team is measured by the client's business results, not by hours. Drive adoption, because a system nobody uses hasn't shipped — and build credibility with the client's engineers and operators
What you'll get:
A senior FDE paired with you on every engagement, a named mentor accountable for your growth
Regular feedback and a clear path to Forward Deployed Engineer
Frontier delivery work across Cowork Activation, Agentic SDLC and Blueprint Activations in Financial Services and Healthcare
Real production experience in regulated industries most early-career engineers never see
Opportunity to work with cutting-edge AI and cloud solutions
Full-time model
Unlimited Vacation policy
Generous health, vision, and dental insurance
401(K) matching plan
Salary base range: $120-145k. 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.
How We Hire
Technical task — Complete a practical engineering task to demonstrate your programming fundamentals, problem-solving skills, and ability to work with AI-powered solutions.
Intro conversation — Discuss your background, projects, and what you're looking for in your next role.
Task discussion — Join a discussion about your approach to the task, what you'd build next, and how you'd evaluate whether it works. No LLMs are permitted during this session.
Why we ask for a task
We're growing and want to find the right fit. The task is a small investment of your time that gives you an opportunity to demonstrate your engineering skills, AI maturity, and ability to think independently. It also helps us verify that we're evaluating a real person rather than a bot. We're not using your work in our products or for client projects.
