Machine Learning Engineer

Overview Descrizione dell'offerta di lavoro.The Italian Institute of Artificial Intelligence for Industry (AI4I) is seeking a skilled and impact-driven Machine Learning Engineer (ML Engineer) to design, deploy, and operate machine learning solutions in real industrial production environments.You will work on high-impact use cases, such as predictive maintenance, anomaly detection, and quality inspection, contributing directly to measurable operational improvements for industrial partners.The role sits at the intersection of data science, software engineering, and industrial AI deployment.Location: AI4I, OGR – Turin, Italy.Hybrid work: Flexible arrangements may be negotiated.Deadline: February 28, ****, 11 : 59 PM CET.About the RoleAs an ML Engineer at AI4I, you will take ownership of end-to-end machine learning pipelines, from data ingestion and feature engineering to model deployment, monitoring, and continuous improvement.You will:Work closely with AI4I's Deployment and Data Science teams, delivering production-grade ML systems to R&D teams, to industrialize promising use cases emerging from applied research.Collaborate with industrial partners, ensuring solutions meet real operational needs and performance targets.Focus on reliable, scalable delivery of measurable results in production environments.Key ResponsibilitiesBuild and deploy ML models for predictive maintenance, anomaly detection, and quality inspection, tied to clear KPIs.Develop and maintain time-series / event data pipelines (ingestion, cleaning, feature engineering, labeling) with reliability and traceability.Run experiments and validate models, define metrics, and monitor production performance (drift / stability).Deploy and scale models on cloud and / or edge using solid MLOps practices (versioning, controlled rollouts).Collaborate with R&D teams to identify and industrialize promising new use cases emerging from applied research.Required QualificationsAt least 5 years delivering AI / data solutions in industrial production (manufacturing, logistics).Strong Python + ML (e.g., scikit-learn, PyTorch / TensorFlow) and solid SQL on relational DBs / data warehouses.Experience with data / pipeline tooling (Airflow, Spark, dbt or equivalents) and end-to-end pipeline ownership.Hands-on experience with at least one hyperscaler (Azure / AWS / etc.) for data pipelines, training and deployment.Docker + Git + reproducible environments (conda / venv).Ability to translate business needs into maintainable DS solutions (EDA, feature engineering, evaluation design), delivering measurable KPIs.Strong communication and stakeholder collaboration in industrial contexts.Preferred QualificationsDegree in Data Science, Computer Science, Engineering, or a related quantitative field.Experience with time-series modeling and forecasting and / or optimization methods applied to industrial problems (e.g. maintenance, quality, throughput, energy).Background in manufacturing, automation, or critical infrastructure, ideally with exposure to IoT and sensor data.Experience with edge or near-edge deployment of models in industrial settings.Experience with model and prompt evaluation, guardrails, safety tooling, and / or AI compliance (e.g. AI Act, internal AI governance).Project and team leadership experience with ownership of delivery.Broad software engineering background (e.g. Java / JavaScript, relational and NoSQL databases, big data and cloud-based systems) and experience in mission-critical service delivery.Proficiency in Italian and English.Additional Strengths (from Candidate Profile)Project and team leadership experience with ownership of delivery.Broad software engineering background and experience in mission-critical service delivery.Key Performance MetricsModel & Use Case Impact: Number and quality of deployed data science solutions and measurable value created (e.g. downtime reduction, quality improvement, forecast accuracy, cost savings).Operational Execution: Reliable, timely delivery of end-to-end data science projects from scoping to deployment.Adoption & Scale: Level of integration, usage, and longevity of models and analytics within client operations.Team Collaboration: Effective cross-team and partner engagement, including clear communication and alignment with industrial stakeholders.Organizational Growth: Contribution to the evolution of the Data Science / Deployment Unit and the Institute's data & AI capabilities.What We OfferA super-smart and creative team of researchers and engineers passionate about AI and robotics.Collaboration, big ideas, career advancement, and the opportunity to co-author papers for top-tier conferences (NeurIPS, ICML, CoRL, etc.).A vibrant office at OGR Torino, access to high-performance computing infrastructure, competitive salary (€60,000 – €80,000 gross per year, including bonus, depending on experience), travel support for conferences and events, and a dedication to your professional growth.How To ApplySubmit your application exclusively through the online form by February 28, ****, 11 : 59 PM CET: Cover letter (max. 1 page) describing how your profile fits this specific position.CV with publications and open-source projects portfolio (e.g., GitHub).About UsThe Italian Institute for Artificial Intelligence (AI4I) was founded as a research institute to perform transformative, application-oriented research in Artificial Intelligence, driving innovation and industrial progress.The Institute engages and empowers gifted, entrepreneurial, and ambitious researchers committed to generating real-world impact at the intersection of science, technology, and industrial transformation.Competitive salaries, performance-based incentives, access to dedicated HPC resources, state-of-the-art laboratories, and strong industrial collaborations are among the distinctive features that define AI4I.AI4I fosters a dynamic international environment, builds an ecosystem that supports innovative startups, and maintains strategic relationships with leading organizations in Italy and abroad to promote Italy's innovation capacity, especially within manufacturing processes and the broader Industry 4.0 value chain.#J-*****-Ljbffr