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About Provectus

Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. 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.

Role Overview: Forward Deployed Engineer (FDE)

You will work in a small, senior pod alongside an FDX. As an FDE, you embed with a client to change how that client operates. You own the method; you map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered.

What you’ll do:

  • Take the seat: Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside and redesign the function from first principles. Reach working fluency in a new domain.
  • Build: Design and ship production GenAI systems into the customer’s environment. Implement and optimize RAG systems for production use cases. Build the evaluation harness before you build the feature. Write production code across the stack — AI, backend services, data pipelines. Take systems to production on AWS (GCP or Azure where the customer requires it). Lead architecture reviews, produce technical design documents, and contribute to standards.
  • Own the outcome: Work in a pair with a FDX who carries the Business Unit’s KPIs. Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here.

What We Offer

  • The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment.
  • A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers.
  • A growing AI delivery practice where you help build the tooling and frameworks, not just use them.
  • Remote-friendly culture.
  • Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance.
  • Career growth; we actively develop our engineers.
  • Access to the latest AI tools and premium subscriptions.
  • Long-term B2B collaboration.
  • Private medical insurance or a budget for your medical needs.
  • Paid sick leave, vacation, and public holidays.
  • Equipment and all the tech you need for comfortable, productive work.

Mindset

  • Proactive and self-directed; identify problems before they're handed to you.
  • Comfort with ambiguity and ownership.
  • B2+ English, comfortable collaborating across distributed, multicultural teams.

Client Engagement

  • Willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code.
  • Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO.
  • You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks.

Technical depth

  • 7+ years building and running production systems.
  • Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes.
  • Designed and shipped to production LLM applications and agentic workflows.
  • Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure.
  • Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
  • Experience building and optimizing RAG systems in production.
  • Strong engineering fundamentals. Python and/or TypeScript proficiency.
  • Experience in making and defending architectural trade-off decisions.
  • Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar.
  • Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines.
  • You have built or owned an eval suite for a non-deterministic system.
  • Model and agent monitoring, drift detection.
  • Cost and latency discipline.
  • Hands-on production experience with the Claude ecosystem.
  • MCP: you can say why an agent would prefer it to a REST integration.
Provectus

Provectus

Job offers 5