Назад до пошукуSenior AI Native Engineering
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AI Native Engineering
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.
Чим ви будете займатися у цій ролі
- 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
Навички
- 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
Буде перевагою
- 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