Lead AI Engineer
Salary not listed • Bucharest • Full-time
Posted 1mo ago
About the job
We are seeking a Lead AI Engineer to provide hands-on engineering leadership for agentic AI products. You will define implementation patterns, ensure technical quality from prototype through production, and lead the technical design and implementation of agents, RAG services, tool integrations, and model orchestration. This role involves establishing coding, testing, evaluation, review, and documentation standards, decomposing architecture into engineering work, guiding estimation and sprint planning, coaching engineers, reviewing code, and resolving complex technical problems. You will also design evaluation suites for quality, safety, reliability, latency, and cost, and work with architects and MLOps to harden solutions for production.
Responsibilities
- Lead technical design and implementation of agents, RAG services, tool integrations, and model orchestration.
- Establish coding, testing, evaluation, review, and documentation standards.
- Decompose architecture into engineering work and guide estimation and sprint planning.
- Coach engineers, review code, and resolve complex technical problems.
- Design evaluation suites for quality, safety, reliability, latency, and cost.
- Harden solutions for production in collaboration with architects and MLOps.
- Contribute reusable methods, patterns, code, assets, and lessons learned to the practice.
- Coach colleagues and participate in the capability's continuous learning agenda.
Requirements
- 6+ years in software, data, or machine-learning engineering, including hands-on AI delivery.
- Strong Python and API engineering capability.
- Experience with modern agent or LLM frameworks.
- Experience with retrieval, embeddings, vector stores, model evaluation, and distributed systems.
- Ability to lead agile engineering teams while remaining hands-on.
- Strong hands-on expertise in Python and API engineering with production experience using agent frameworks (e.g., LangChain, LangGraph, Microsoft Agent Framework, Semantic Kernel, OpenAI Agents SDK, AutoGen, CrewAI).
- Ability to implement graph-based and code-first orchestration patterns (state, memory, checkpoints, tool calling, hand-offs, retries, idempotency, human approval, long-running workflows).
- Advanced experience with RAG, structured outputs, prompt and context engineering, embeddings, vector or hybrid retrieval, reranking, knowledge graphs, and retrieval evaluation.
- Experience integrating agents with enterprise systems via REST/GraphQL APIs, events, queues, databases, and MCP-compatible tools/servers.
- Practical experience with automated evaluation and observability (e.g., LangSmith, MLflow, Langfuse, OpenTelemetry, Azure AI evaluation capabilities) covering quality, trajectory, latency, token use, and cost.
- Strong software engineering discipline across testing (e.g., pytest), type checking, code review, dependency management, secure coding, CI/CD, and containerized deployment.