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Provectus

Associate Forward Deployed Engineer

Posted Yesterday
Remote or Hybrid
2 Locations
120K-145K Annually
Junior
Remote or Hybrid
2 Locations
120K-145K Annually
Junior
Build and deploy production LLM applications, agentic workflows, retrieval and extraction pipelines, and evaluation harnesses for regulated-industry clients. Work alongside senior engineers and client teams to understand business processes, write production code on AWS, measure system performance, drive adoption, and improve reusable AI Blueprints. This early-career role includes client collaboration, domain learning, code reviews, and developing ownership of scoped production-system components.
The summary above was generated by AI

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.
  •  

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.
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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 baserange $120-145k. The salary range is determined through interviews and a review of the education, experience, knowledge, skills, abilities of the applicant, and alignment with market data.
  •  

How we hire:

  • Intro conversation — your background and what you want to do.
  • One live engineering session — a real problem in your own editor. You may use an LLM assistant (ChatGPT, Claude); autocomplete and agentic coding tools are off.
  • The learning session — we describe an unfamiliar business function. Talk us through how you'd learn it, what you'd ask the person who does it, and what you might build. No LLMs for this one. Team conversation.
  •  

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