Senior Staff AI Engineer Golang, Python

AI in production
- Design, build and operate LLM-powered features end to end: agents with tool or function calling, RAG over internal data and documents, and structured outputs. Integrate them into Go microservices over gRPC/REST.
- Build evaluation into every AI feature: offline eval sets, regression tests, LLM-as-judge where it fits. Monitorquality, latency and cost in production.

- Own AI safety and data governance: scope data access by user permissions, expose only allowlisted fields to models, defend against prompt injection, and handle sensitive data correctly.
- Build data and algorithm pipelines on event streams (Kafka/CDC) and relational data. Backtest recommendations against historical data before go-live.

Backend engineering
- Build and maintain high-throughput, mission-critical Go services in a microservice, event-driven architecture.
- Tune performance across services, databases and caches. Troubleshoot production incidents and drive refactoring.
- Write clean, tested, maintainable code. Review code and raise the engineering bar of the team.

Engineering with AI
- Use AI coding agents every day across the development lifecycle: spec, code, test, review, debug.
- Build internal tools, prompts and workflows that make the whole team faster, while keeping quality under control.

Collaboration
- Work with Product Owners, business and operations teams to turn vague problems into measurable AI solutions.
- Explain model results and trade-offs in plain business language.

Must have

• Bachelor's degree in Computer Science, IT or equivalent.
• 4+ years of backend development, including at least 3 years building production Go applications.
• Strong distributed-systems fundamentals: microservices, event-driven architecture, Kafka or a similar message broker, gRPC/REST, concurrency and parallel programming.
• Solid database skills: PostgreSQL (query design, indexing, performance tuning), at least one NoSQL database, and distributed caching such as Redis.
• Hands-on experience shipping at least one LLM-based feature to production, not only a prototype. That includes prompt design, tool or function calling, RAG and structured outputs, plus a clear understanding of the limits: hallucination, latency, token cost.
• Hands-on experience with agent frameworks, Model Context Protocol (MCP) and multi-agent systems, and with vector search (e.g. pgvector) for retrieval.
• A measure-first mindset: you prove an AI or algorithm change with data and evaluation before calling it done.
• Good data skills (SQL, Python) to analyse data, build datasets and run backtests.
• Ability to drive technical solutions independently and troubleshoot complex issues with minimal supervision.
• Daily use of AI coding tools (e.g. Claude Code, Cursor, Copilot) and good judgement on when to trust their output.
• Good cross-team communication skills.

Nice to have

• Classic ML or optimisation (forecasting, recommendation, operations research).
• Big-data and OLAP technologies (e.g. BigQuery, ClickHouse).
• Docker, Kubernetes and cloud platforms (GCP , AWS).
• Frontend work with React/TypeScript when needed.
• Domain knowledge in logistics, payroll or compensation systems.

• 13th-month salary, performance bonuses, and KPI bonuses
• Health insurance, Social insurance, and Unemployment insurance
• Medical insurance
• Annual health checkup
• Annual salary reviews
• Annual team building/company trip
• Ongoing internal and external training opportunities.