[Hiring] Senior AI Native Engineering @EPAM Systems

Role Description

We're looking for a Senior AI-Native Engineer to join the AI-native team. This role is about designing AI systems that other engineers and customers depend on, taking them to production, and measuring whether they work. The role works the AI-native way: spec first, tests first, coding agents doing implementation under review.

• Design and run production LLM systems: multi-stage pipelines or agents with a curated tool set, output schemas, validation, retries and failure recovery
• Build and maintain evaluation: golden datasets, regression evals on every prompt change, LLM-as-judge with spot checks, production telemetry
• Decide where work is autonomous, where it assists, and where a person approves
• Deliver spec-first and test-first with coding agents, and keep project context files (AGENTS.md, CLAUDE.md, steering files) deliberate and token-aware
• Put AI steps into CI/CD (review gates, test generation, quality hooks) and tune them against false positives
• Estimate AI cost before building, measure it after, and use the cost levers (model routing, context trimming, caching, batching, limits)
• Publish reusable skills, prompts, tool schemas, eval sets and MCP servers, and keep them current
• Raise AI failures early and fix them structurally. Help new teammates adopt the team's AI workflow

Qualifications

• At least one AI workflow or system running in production or CI without daily attention, which the candidate can explain: trigger, users, own part, and how they know it works
• Multi-step LLM or agent design experience: tool restriction, output schemas, validation, fallbacks, latency trade-offs
• An evaluation approach the candidate built or extended
• A wrong output fixed with a structural change, not a re-run
• Knows what drives the cost of their own AI work and has used at least one cost lever with a number on the effect
• 5+ years of professional software engineering with production ownership
• 2+ years building with LLMs or agents on real projects
• Clear communication: a peer could rebuild their work from their description

Requirements

• Nice to have: Multi-agent systems in production
• Brownfield discovery on a legacy codebase
• AI steps in CI/CD with measured outcomes (PR cycle time, defect density, AI acceptance rate)
• More than one model provider or platform with reasons for each
• Tracing and observability for LLM systems
• Experience in security-sensitive environments