FDE AI/ Solutions Architect (AI, Python/Data)

    ~$57,512 - $106,808Market Estimate
    Tbilisi && Yerevan && Belgrade && Novi Sad, Vojvodina && Poland && Portugal
    Full-Time
    Mid (3-6 yrs)
    Engineering & Development
    Posted on June 30, 2026
    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. 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 Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.

    Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements.

    Where this role sits:

    You will work in a small, senior pod alongside an FDX; our delivery arc is Sprint → Enable → Realize:

  1. Forward Deployed Executive (FDX) owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction.

  2. Forward Deployed Engineer (FDE) embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. 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. Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered.

  3. Requirements:

    Mindset

  4. Proactive and self-directed; identify problems before they're handed to you

  5. Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job

  6. B2+ English, comfortable collaborating across distributed, multicultural teams

  7. Client Engagement

  8. You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code

  9. Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them

  10. You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run

  11. Technical depth

  12. 7+ years building and running production systems.

  13. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes

  14. Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks

  15. Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure

  16. Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.

  17. Experience building and optimizing RAG systems in production

  18. Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack.

  19. Experience in making and defending architectural trade-off decisions

  20. Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus

  21. Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines

  22. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release

  23. Model and agent monitoring, drift detection

  24. Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs

  25. Hands-on production experience with the Claude ecosystem — Claude Code, CLAUDE.md, hooks, skills files. Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus

  26. MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus

  27. What you’ll do:

    Take the seat

  28. Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles.

  29. Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow.

  30. Build

  31. Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases

  32. Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design.

  33. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.

  34. Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client.

  35. Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from.

  36. Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team.

  37. Own the outcome.

  38. Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number.

  39. Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here.

  40. Be credible with the customer’s engineers and their executives.

  41. Shape what we commit to before we commit to it.

  42. Nice to Have:
  43. Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution

  44. Experience in one of the industries: financial services, insurance, healthcare

  45. Consulting, professional services, or other embedded customer-facing delivery

  46. A2A: you can explain agent-to-agent interoperability

  47. AWS and Claude Code Certifications

  48. CI/CD pipeline experience (GitHub Actions, GitLab CI)

  49. Experience in an additional language (Go, TypeScript, or Rust)

  50. Experience with Apache Spark, Apache Airflow, Kafkа

  51. What We Offer:
  52. The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment

  53. A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers

  54. A growing AI delivery practice where you help build the tooling and frameworks, not just use them

  55. Remote-friendly culture

  56. Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance

  57. Career growth; we actively develop our engineers

  58. Access to the latest AI tools and premium subscriptions

  59. Long-term B2B collaboration

  60. Private medical insurance or a budget for your medical needs

  61. Paid sick leave, vacation, and public holidays

  62. Equipment and all the tech you need for comfortable, productive work

  63. How we hire:

    1. Intro conversation. The role, your background and aspirations, tech questions.

    2. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant

    3. HR Interview. Soft skills and expectations

    4. HM interview. Tech questions; a live engineering session is also possible

    Company:  Provectus

    AI systems integrator building and operating production AI and data systems for enterprises.
    201-500 employees
    AI & Machine Learning
    HQ: United States