Senior Machine Learning Engineer

• Scope: Tech Lead / Lead Engineer for machine learning solutions, MLOps, and deployment
• Salary range: €70,000 – €90,000 gross/year (depending on experience)
• Location: Turin

About AI4I and our culture

The Italian Institute of Artificial Intelligence (AI4I) is headquartered in the standout location of OGR, Turin , with the unique mission to support both applied research and on-site deployment of AI . We are established to generate real-world impact at the intersection of science, technology, and industrial transformation.

Research units focus on topics such as secure and trustworthy AI, physical AI and multi-agent systems while the deployment unit is responsible for the implementation at client site. A dedicated high-performance computing center , including next-generation GPU systems such as NVIDIA B200 accelerators, is available to support both research and industrial deployment.

Our System User Knowledge platform and OGR-based network connect corporates, SMEs, startups, and scientific partners, fostering workshops and collaboration with external partners to remain at the forefront of innovation.

Role Summary

Within the Deployment Unit, we are hiring a Senior Machine Learning Engineer who will operate as a hands‑on tech lead for client projects owning of end‑to‑end machine learning pipelines , from data ingestion and feature engineering to model deployment , monitoring, and continuous improvement. This is a highly hands‑on role for someone who combines strong software and data engineering skills with deep knowledge of machine learning methods and industrial use cases such as predictive maintenance, data quality control, anomaly detection, and operational performance improvement.

You will work closely with:

• Deployment and Data Science team , delivering production‑grade ML systems
• Research units , to industrialize promising use cases emerging from applied research
• Industrial partners , ensuring solutions meet real operational needs and performance targets

What you will do

• Act as a tech lead in projects and scope them and their technical feasibility through client interaction.
• Build and deploy ML pipelines for problems such as predictive maintenance, anomaly detection, process optimization, quality control and forecasting, tied to clear KPIs.
• Develop and maintain time‑series/event data pipelines (e.g., ingestion, cleaning, feature engineering, labeling) with reliability and traceability.
• Perform data exploration, feature engineering, model selection, hyperparameter tuning, validation, and performance analysis using appropriate ML techniques .
• Design robust solution architectures to run experiments and validate models integrating data pipelines, ML models, APIs, orchestration tools on cloud/on‑prem infrastructure.
• Deploy and scale models on cloud and/or edge using solid MLOps practices (i.e., establishing processes for monitoring, retraining, drift detection, performance tracking, and lifecycle management).
• Apply agentic AI‑assisted coding to reduce cycle time and improve results across the above responsibilities

About you

• Master’s degree in Computer Science, Engineering, Physics or related; PhD a plus.
• Strong hands‑on experience (5+ years) building and deploying machine learning applications based on traditional ML methods, including regression, classification, anomaly detection and forecasting.
• Experience with optimization methods for industrial production (e.g., manufacturing, logistics).
• Strong Python + ML (e.g., scikit‑learn, PyTorch/TensorFlow) and solid SQL on relational DBs/data warehouses and ETL/ELT pipelines .
• Strong software engineering background and experience with APIs and backend development, testing and debugging, Git, CI/CD pipelines, containers and microservices.
• Use agentic AI‑assisted coding workflows daily (e.g., Claude Code, Codex)
• Experience with end‑to‑end pipeline ownership leveraging production deployment patterns such as data pipelines, orchestration (e.g., Airflow), scheduled jobs, monitoring and observability with Prometheus, Grafana, or OpenTelemetry, and MLOps practices using tools such as MLflow, Docker and Kubernetes.
• Hands‑on experience in designing cloud enterprise solutions with at least one hyperscaler (Azure/ AWS/ etc.) for data pipelines, training and deployment.
• Proficiency in Italian and English .

Nice to Have

• Experience with computer vision for object recognition and quality assessment is a strong plus.
• Experience with time series analysis and forecasting from operational systems, sensors and machines .
• Experience with edge analytics or deployment in constrained industrial environments .
• Background in manufacturing , automation, or critical infrastructure, ideally with exposure to IoT and sensor data .

We encourage you to apply even if you do not believe you meet every single qualification . Not all strong candidates will meet every single qualification as listed.

What we offer

• Access to our advanced computing infrastructure
• A team with engineers and researchers working together on real industrial AI deployments
• Chance to co‑author papers for top‑tier conferences like NeurIPS, ICML, CoRL, and RSS
• Exposure and collaboration with a huge network of corporates, SMEs, startups and technology partners
• An exceptional workplace @OGR, Turin at the epicenter of tech
• Lots of learning opportunities : IAS, internal Academy, budget for events, conferences and online courses
• Relocation incentives and competitive benefits package (including potential tax advantages for international candidates, where applicable)

Selection process

• Fit interview
• Technical live assessment
• Final motivation interview

How to apply

Submit your application exclusively through the online form including:

• CV
• Cover letter (max. 1 page) describing how your profile fits the position and personal projects , if any
• Optional, links to code repositories (e.g., GitHub), personal webpage or projects (e.g., open‑source contributions)
• Optional, names of two references

To ensure a timely process, we will only be in touch with candidates who progress to the interview stage.