GenAI Engineer | Prague
Deloitte Zobrazit všechny práce
- Praha
- Trvalý pracovní poměr
- Plný úvazek
- 2+ years as a GenAI Engineer; ideally some NLP background for RAG and Talk2Document work.
- Python-first engineer who can build production APIs/services (FastAPI or similar).
- You’ve built agentic systems with LangGraph / LangChain / PydanticAI / CrewAI (or similar).
- You’re comfortable integrating tools/data safely (timeouts, retries, idempotency, rate limiting).
- You’ve worked with RAG/vector search (Azure AI Search, Pinecone, Redis Vector, Milvus/Chroma).
- You’ve shipped Talk2Data and/or Talk2Document-style solutions (or very similar patterns).
- You care about quality: eval harnesses + regression suites, and you can use LangSmith/Langfuse/Datadog to debug what’s happening.
- Familiar with MCP and secure model-to-tool/data connectivity.
- Bonus: A2A patterns / agent-to-agent coordination.
- Bonus: agent builder frameworks (Azure AI Foundry, AWS Bedrock Agents, Vertex AI Agent Builder, or similar).
- Cloud experience: Azure preferred, AWS/GCP fine.
- Agent workflows with LangGraph / LangChain / PydanticAI / CrewAI (routing, retries, fallbacks, timeouts, human-in-the-loop).
- Tool calling that doesn't break: APIs, databases, and internal services with clean contracts, predictable behavior, and safe error handling (retries/timeouts, idempotency, rate limiting).
- Hybrid systems: pre-built agents plus a thin custom orchestration layer (interfaces, policies, guardrails, reuse).
- MCP integrations: implement MCP servers/clients so models can safely access tools/data (DBs/APIs/files) using least-privilege patterns and audit-friendly logging.
- RAG + knowledge systems: chunking, embeddings, indexing, retrieval strategies, grounding patterns; vector stacks like Azure AI Search, Pinecone, Redis Vector, Milvus/Chroma; doc ingestion with Azure Document Intelligence.
- Talk2Data (safe querying + interpretation of enterprise data)
- Talk2Document (Q&A/summarize/extract/reason over docs with citations/grounding)
- Evaluation + observability: automated evals (task success, groundedness/relevance, safety, latency, cost), regression suites, and run tracing for debugging using LangSmith / Langfuse / Datadog.
- Cloud GenAI: Azure OpenAI / Azure AI Foundry + Azure AI Search / Document Intelligence / Content Safety. AWS/GCP equivalents welcome (Bedrock/Vertex AI + search/document pipelines).
- A global network + a strong regional AI&D community (~160 people).
- A dedicated technical team that's actively growing and open to experimenting with new tech (when it actually helps).
- Consultancy work, but on real problems with real impact - solutions people use.