Senior AI Engineer

25 ott - Monza
msg group

Overview As part of the msg group, you will translate business processes into reliable, production-ready AI agents and systems. You design end-to-end pipelines, selection of tools, and human handoffs to ensure accuracy and trust in deployed models. You focus on turning complex domain logic into testable, measurable solutions with clear ownership of quality.

This role combines engineering rigor with business impact, delivering scalable retrieval, grounding, and evaluation capabilities. A meaningful chance to shape how AI touches real work for leading enterprises.

Retribuzione / Benefits competitive salaries 55.000 - 65.000 Eur broad range of benefits diversity and cultural differences are cherished

Responsabilità Decompose business processes into steps, tools, handoffs, and decision points for agent-based execution

Design and implement retrieval pipelines with chunking, embedding models, hybrid search, and grounding to source data

Treat prompt and context engineering as an engineering discipline with versioning, tests, and measurement

Build the evaluation layer with datasets, automated scoring, and regression suites

Instrument production systems for observability: traces, latency, costs, failure rates, and drift

Own end-to-end quality and diagnose failures to their root cause across retrieval, reasoning, tooling, or data

Balance model quality, cost, and latency, adapting choices as models evolve

Collaborate with business experts to extract domain logic and translate it into testable artifacts





Take agents into production with platform and infrastructure teams and monitor live behavior

Requisiti fondamentali 5+ years software development experience, 2+ years with production LLM-based systems

Strong Python fundamentals: version control, tests, code reviews, incremental delivery

Experience with async work, FastAPI and Pydantic or equivalents

Hands-on with at least one agent orchestration framework (LangGraph, LlamaIndex Workflows, OpenAI Agents SDK, Temporal)

Experience serving models via managed platforms (AWS Bedrock, Azure AI Foundry, Google Vertex AI) or provider APIs with streaming, structured output, prompt caching, rate-limiting

Production vector search experience (pgvector, Qdrant, Pinecone, Weaviate, Milvus, Azure AI Search) with understanding of trade-offs

Deep retrieval expertise (chunking, metadata filtering, query rewriting, hybrid search, reranking)

Fluent in tool and function calling, structured output, and multi-step agent design

Hands-on with Model Context Protocol (MCP): building servers, tool interfaces, auth and error handling

Evidence of a solid evaluation discipline and a measurable eval harness

Ability to work with business experts to produce workable specifications

Judgment to select appropriate tools vs rules engines and plain queries

Fluent English comfort with ambiguity strong communication with business stakeholders collaboration across cross-functional teams

Python

FastAPI

Pydantic

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