Mid-Level, Senior
AI Systems Engineer
We are one of the leading technology groups in Italy. We operate as a Business & Technology Accelerator for companies, financial institutions, and public sector organizations, supporting the evolution of their IT and digital strategies. Through an ecosystem of companies that share values, business strategies, and a common vision, we enable our clients to unlock the full potential of technology, amplifying its positive impact on both the economy and society.
What to expect
You are the "Engine Builder" of our generative AI and machine learning practice. You will synthesize LLM application development, MLOps, and Data Engineering to build the systems our clients rely on. From constructing tool-augmented AI agents that parse chaotic enterprise data, to fine-tuning custom models for niche client needs, to setting up the CI/CD pipelines that deploy them, you ensure that our AI solutions actually work in production as desired.
What you will do
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Build robust LLM applications, RAG (Retrieval-Augmented Generation) systems, and multistep agentic workflows that can be tailored for different client use cases.
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Design and manage the MLOps and orchestration pipelines (e.g., Airflow, Dagster, Kubeflow) that ingest data, trigger models, and deliver outputs reliably.
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Evaluate when to use massive commercial APIs and when to fine-tune, distill, or host smaller open-source models (Llama, Mistral) to meet client privacy, latency, or cost constraints.
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Containerize ML and LLM microservices (Docker/Kubernetes), expose them via secure APIs (FastAPI), and manage their deployment in client cloud environments (AWS/Azure/GCP).
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Build configuration-driven platforms where logic, prompts, and processing nodes can be easily tweaked by our consultants without rewriting the core software for every new client.
What we expect from you
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3-5 years of software or data engineering, with deep, hands-on experience building and deploying LLM applications and ML pipelines in production.
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Mastery of Python, modern LLM frameworks (LangChain, LlamaIndex, DSPy), and vector databases.
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Proven ability to make ML pipelines work reliably using workflow orchestration tools, containerization, and monitoring systems.
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Hands-on experience with LLM fine-tuning techniques (LoRA, PEFT) and hosting open-weight models.
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Strong foundation in API design, Git, testing, and system architecture.
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Nice to Have: Experience working in an IT services or consulting environment, rapidly prototyping solutions for diverse clients; Familiarity with data evaluation frameworks (Ragas, TruLens) to prove AI accuracy to clients.
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