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

Provectus

Remote · ArmeniaSalary not disclosedfull-timeVerified recentlyProvectus Careers

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

Responsibilities

Take the seat
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.
Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow.
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
Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design.
Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.
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.
Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from.
Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team.

Languages

Russian

Work format: Remote
Seniority: Senior
Posted: 15 Apr 2026 (4mo ago)
Last verified: 28 Aug 2026

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Responsibilities

Take the seat

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.

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

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

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

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

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.

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

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