PwCPosted 6mo ago

AI Engineering Manager (Full-Stack) - Milano

AI Engineering Manager (Full-Stack) - Milano at PwC scores 65 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.

MilanFull time

AI in this role

langchaindatabricks

prompt-engineeringragml-ops

Line of Service

Advisory

Industry/Sector

Not Applicable

Specialism

Data, Analytics & AI

Management Level

Manager

Job Description & Summary

Entra a far parte della practice Data&Cloud di PwC Italy e diventa:

AI Engineering Manager Full-Stack

Descrizione del ruolo

Entrando nel team AI Engineering di PwC come profilo Senior, avrai un ruolo chiave nella guida di programmi di trasformazione digitale complessi per primari player nei settori Automotive & Industrial Manufacturing, Consumer Markets, Energy & Utilities, Healthcare, Financial Services e Telco & Media.

Sarai responsabile della progettazione architetturale, della leadership tecnica e del coordinamento di team multidisciplinari, contribuendo alla definizione di soluzioni AI e Data Engineering scalabili, innovative e ad alto impatto sul business.

Responsabilità principali

Guidare workshop con i clienti per identificare opportunità di trasformazione AI e tradurre esigenze di business in architetture scalabili e robuste

Disegnare e supervisionare la realizzazione di data platform e pipeline complesse su ambienti cloud (AWS, Azure, GCP, Palantir, Databricks)

Definire standard architetturali, best practice e framework di sviluppo per soluzioni AI e Data Engineering

Governare processi CI/CD/CT, DevOps e MLOps assicurando automazione, monitoraggio e continuità operativa

Progettare e validare soluzioni avanzate di Generative AI (LLM, RAG, Agentic AI, AI copilots, chatbot enterprise)

Supervisionare lo sviluppo di soluzioni end-to-end, dalla data ingestion fino alle interfacce utente (React, Streamlit, low-code)

Contribuire ad attività di pre-sales, proposal e definizione di roadmap tecnologiche, oltre che alla gestione del team dal punto di vista operativo e di crescita

Requisiti richiesti

Laurea in discipline STEM (preferibilmente Informatica, Ingegneria Informatica, Matematica, Statistica)

Esperienza pluriennale in AI Engineering, Data Engineering o ML Engineering

Conoscenza approfondita di Python, Spark/PySpark, SQL e architetture distribuite

Esperienza consolidata su almeno una piattaforma cloud (AWS, Azure, GCP, Palantir, Databricks)

Competenza avanzata in Generative AI (LLM, Prompt Engineering avanzato, RAG, Agentic AI, orchestrazione multi-agent)

Conoscenza di framework per lo sviluppo di soluzione agentiche (langchain, langraph, agno, Google ADK, Microsoft Agent Framework)

Education (if blank, degree and/or field of study not specified)

Degrees/Field of Study required:

Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, AI Implementation, Analytical Thinking, C++ Programming Language, Coaching and Feedback, Communication, Complex Data Analysis, Creativity, Data Analysis, Data Infrastructure, Data Integration, Data Modeling, Data Pipeline, Data Quality, Deep Learning, Embracing Change, Emotional Regulation, Empathy, GPU Programming, Inclusion, Intellectual Curiosity, Java (Programming Language), Learning Agility {+ 30 more}

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Not Specified

Available for Work Visa Sponsorship?

Government Clearance Required?

Job Posting End Date

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRagMl OpsLangChainDatabricks

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How do you monitor a model once it's live, and how do you know it needs retraining?
What's a project where you used LangChain hands-on?
Walk me through how you've used Databricks in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Prompt Engineering, Rag, Ml Ops, LangChain, and Databricks. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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