AI Engineer - Internship (Open also to Protected Categories, Law 68/99)

AI in this role

fine-tuningcomputer-visionnlp

Location: Gorgonzola, Italy

In fast changing markets, customers worldwide rely on Thales. Thales is a business where brilliant people from all over the world come together to share ideas and inspire each other. In aerospace, transportation, defence, security and space, our architects design innovative solutions that make our tomorrow's possible.

Gorgonzola hosts Navigations Air Systems (NAS) domain. The activity of NAS domain: avionics, navigation and radar systems for civil air traffic and technologies supporting the activities of civilian and military pilots, ATC controllers and technicians of several airlines, air traffic control agencies and Air Force around the world. Italy is the Group’s Competence center for the design, production and delivery of Navigation Aids products and Competence Centre for the delivery of non-Radar Surveillance Products.

Job title: AI Intern

As part of our digital transformation, we are launching an innovative Proof of Concept to develop an AI-assisted tool capable of automating the initial drafting of an FMEA. The AI should analyze unstructured and structured exports (e.g. BOMs) and cross-reference them with historical reliability files to predict failure modes and their local effects.

Key Responsibilities

In the Operations team, you will:

Analyze electronic data: understand and parse unstructured (pdf drawings) and structured outputs from standard EDA software (e.g., Mentor Graphics, Cadence).
Develop an AI/Data pipeline: design and implement a Python-based AI strategy, combining NLP and Computer Vision techniques (LLMs and LVMs) with graph-based approaches to map circuit topologies and understand component relationships.
Integrate historical data: connect your algorithm to historical failures files to accurately predict how specific components (e.g., resistors, capacitors, ICs) fail within their specific circuit context.
Generate FMEA reports: structure the AI’s output into a standardized FMEA format that human engineers can review and validate.
Test and validate: Work closely with hardware engineers to validate the AI's logic on a simple, baseline electronic board to prove the concept's viability.

Requirements:

Currently pursuing a Master’s degree or in the final year of an engineering School. Preferred majors: Electrical Engineering or Computer Science /AI.
Strong programming skills in Python.
Experience with AI/Machine Learning concepts (NLP, LLM prompting/fine-tuning, or data structuring).
Fundamental understanding of electronic circuits, components, and schematics.
Familiarity with EDA tools and file formats (Netlists, BOMs) is a plus.
Basic knowledge of reliability engineering concepts or FMEA is advantageous.

Soft Skills

Hybrid Thinker: Ability to bridge the gap between hardware engineering and software/AI development.
Problem Solver: A pragmatic approach to scoping AI projects (starting simple and scaling up).
Autonomous & Curious: Eager to dive into historical technical data and experiment with new algorithmic approaches.
Good communication skills to present your findings to both software and hardware teams.

At Thales we provide CAREERS and not only jobs. With Thales employing 80,000 employees in 68 countries our mobility policy enables thousands of employees each year to develop their careers at home and abroad, in their existing areas of expertise or by branching out into new fields. Together we believe that embracing flexibility is a smarter way of working. Great journeys start here, apply now!

How we score this

AI Engineer - Internship (Open also to Protected Categories, Law 68/99) at Thales scores 46 out of 100 on AI centrality, which makes it AI Level 2 of 4 (Uses AI) on this board. The level measures how much of the work is AI, not seniority.

Classification

AI Level 2. An ordinary role whose duties require using AI tools, such as coding, writing or sourcing with an assistant.

AI Level 480 to 100
AI Level 360 to 79
AI Level 240 to 59
AI Level 10 to 39

Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

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

Fine TuningComputer VisionNlp

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
This role expects you to use AI tools as part of the job. Which ones have you used, and for what?
Tell me about a time an AI tool got something wrong. How did you catch it?

Adapt your resume

List these exact terms on your resume: Fine Tuning, Computer Vision, and Nlp. 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.
Put the AI tool in a bullet point about what you did, not just in a skills list — this role treats it as a required part of the job.

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