GenAI Engineer | Hungary

Description & Requirements

Who we are looking for

We’re building agentic systems that do real work in production: tool-using workflows, RAG, structured outputs, guardrails, and measurable quality.If you like shipping and iterating fast - but still care about doing it properly - this is the team.

You’ll work with Solution Architects and LLMOps, but your focus is simple: build the thing, make it reliable, and keep improving it.

What you bring:

• 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.
• EU work permit

Your future role

What you will build:

• 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.
• Real use cases:
• 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).

What we offer

• 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 onreal problems with real impact - solutions people use.
• Specialize in specific areas where we are committed to support the learning process and obtaining internationally recognized certificates
• Hybrid way of work
• International environment where personal development and growth are supported and highly encouraged
• Unique opportunity for a career and promotions