Deloitte

Audits companies and advises on tax and technology

GenAI Engineer

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeHungaryNo salary listed

About the Job

Requirements

At least 2 years of experience as a Generative AI Engineer.
Some natural language processing background for retrieval-augmented generation and Talk2Document work is ideal.
Ability to build production application programming interfaces and services, using Python and FastAPI or similar.
Experience building agentic systems with LangGraph, LangChain, PydanticAI, CrewAI, or similar frameworks.
Ability to integrate tools and data safely, including timeouts, retries, idempotency, and rate limiting.
Experience with retrieval-augmented generation and vector search, using Azure AI Search, Pinecone, Redis Vector, Milvus, Chroma, or similar.
Experience shipping Talk2Data and/or Talk2Document-style solutions or similar patterns.
Experience with evaluation harnesses and regression suites, and ability to debug with LangSmith, Langfuse, Datadog, or similar tools.
Familiarity with Model Context Protocol and secure model-to-tool/data connectivity.
Cloud experience; Azure is preferred, and AWS or Google Cloud Platform experience is acceptable.
An EU work permit is required.

Responsibilities

Build agent workflows using LangGraph, LangChain, PydanticAI, CrewAI, or similar, including routing, retries, fallbacks, timeouts, and human-in-the-loop processes.
Implement reliable tool calling with APIs, databases, and internal services, using clean contracts, predictable behavior, and safe error handling such as retries, timeouts, idempotency, and rate limiting.
Develop hybrid systems combining pre-built agents with custom orchestration layers, interfaces, policies, guardrails, and reusable components.
Implement Model Context Protocol servers and clients to let models securely access tools and data, using least-privilege patterns and audit-friendly logging.
Build retrieval-augmented generation and knowledge systems, including chunking, embeddings, indexing, retrieval strategies, grounding patterns, vector search, and document ingestion with Azure Document Intelligence.
Build Talk2Data solutions for safe querying and interpretation of enterprise data.
Build Talk2Document solutions for answering questions, summarizing, extracting, and reasoning over documents with citations and grounding.
Develop automated evaluations for task success, groundedness and relevance, safety, latency, and cost; maintain regression suites and run tracing for debugging using LangSmith, Langfuse, Datadog, or similar tools.
Build cloud Generative AI solutions using Azure OpenAI, Azure AI Foundry, Azure AI Search, Document Intelligence, and Content Safety, or equivalent AWS Bedrock or Google Vertex AI and search/document pipelines.

Desired Qualifications

Experience with agent-to-agent patterns and coordination.
Experience with agent-builder frameworks such as Azure AI Foundry, AWS Bedrock Agents, Vertex AI Agent Builder, or similar.

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 on real 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

#LI-AN2

About the Company

Deloitte

Audits companies and advises on tax and technology

Deloitte audits companies and advises businesses and governments on tax, technology, and risk, and it is one of the Big Four, the world's four largest accounting firms. In 1849 its founder became the first independent auditor of a public company, Britain's Great Western Railway. Its auditors check companies' financial reports for errors and fraud before investors rely on them, while its consultants design strategy, install business software, and defend clients against cyberattacks. Clients pay fees for each audit or project, and each country's Deloitte firm is a separate business owned by its local partners. Deloitte was founded in London in 1845 and is based in New York.

Company size: 10,001+
Company stage: Late Stage VC
Total funding: $17.1M
Headquarters: New York, NY
Founded: 1845

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