Senior Engineer, ML/Data

Job description

The Impact You'll Make Do you enjoy turning complex data into scalable solutions that enable smarter products and better decisions? Are you passionate about data engineering, machine learning and building production-ready AI capabilities? Then this could be the opportunity for you. As our Senior Engineer, ML/Data , you’ll play a key role in building the data and AI foundation that supports advanced analytics and intelligent solutions across Danfoss Climate Solutions. Your work will help transform operational, sensor, product and business data into reliable, AI-ready data products and production machine learning workflows. Working closely with data scientists, platform engineers and business teams, you’ll combine strong hands-on engineering skills with an MLOps mindset, helping us evolve toward a connected and scalable data ecosystem that enables energy optimization, predictive insights and smarter decision-making. What You’ll Be Doing Design, build and maintain scalable data pipelines and ML-enabled data products, integrating sensor, device, product and business data into a reliable data platform Develop and improve data ingestion, transformation and orchestration workflows using technologies such as Airflow, Mage or Dagster Collaborate with data scientists, engineers and domain experts to create high-quality, model-ready datasets and reusable foundations for analytics and AI use cases Support the deployment, monitoring and operation of machine learning solutions in production, including KServe-based model serving and MLOps workflows Build and improve automation for data quality, feature pipelines, model retraining and deployment, ensuring reliable and maintainable production workflows Contribute to semantic data models and knowledge graph capabilities that enrich analytics and AI use cases across different business areas Continuously improve the scalability, observability, reliability and maintainability of our data and ML platform through automation and strong engineering practices What We're Looking For Strong hands-on experience in data engineering, machine learning engineering or similar roles, with solid Python and SQL skills Experience building and orchestrating production data pipelines using tools such as Airflow, Mage or Dagster Experience with cloud platforms such as Azure, AWS or GCP, together with Docker and Kubernetes Good understanding of MLOps, production model deployment and modern data architectures; experience with KServe is considered an advantage Strong problem-solving and collaboration skills, with the ability to work effectively across technical teams and stakeholders Experience with industrial IoT data, HVAC, energy systems, knowledge graphs or semantic data models is considered an advantage Ready to Make a Difference? If this role excites you, we’d love to hear from you! Apply now to start the conversation and learn more about where your career can go with us. Our Commitment to Transparency Salary ranges listed reflect only primary location and currency. For local salary ranges and specific benefits in your preferred hiring location, please ask your Talent Acquisition representative for more information. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience, and location. Any benefits listed do not promise or guarantee any particular benefit or specific action. They may depend on country or contract specifics and are subject to change at any time without prior notice. Want to know more? Please visit our FAQ Page . All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or other protected category.