Design, implement, and maintain scalable ML pipelines for training, validation, deployment, and monitoring in silicon development. Develop and deploy Generative AI solutions, including LLM fine-tuning, embeddings-based search, and RAG pipelines for silicon workflows. Build robust data engineering solutions (ETL, integration, warehousing, data lakes, feature stores) supporting ML and Generative AI. Optimize ML infrastructure with containerization (Docker, Kubernetes), cloud resources (AWS SageMaker, Azure ML), and CI/CD. Establish rigorous ML lifecycle management with version control, automated testing, validation, and performance monitoring. Implement and optimize data storage (SQL/NoSQL, data lakes) for silicon analytics workloads. Promote ethical and responsible ML engineering with transparency, reproducibility, security, and strong governance. Mentor peers on ML engineering, data engineering, and Generative AI best practices for PEU Silicon. Stay updated on emerging ML engineering, Generative AI, and data infrastructure trends, recommending innovative solutions. Collaborate with the Silicon Infrastructure and Tools team to align ML/data infrastructure planning, compute resources, and licensing strategies Bachelor's/Master's degree in computer science, Machine Learning, Electrical Engineering, or related fields (preferably with distinction). Minimum 3+ years of professional experience deploying ML systems, Generative AI solutions, and data engineering pipelines in production environments. Strong programming skills in Python, expertise with ML frameworks (TensorFlow, PyTorch, LangChain, Spark ML). Hands-on expertise with Generative AI technologies (e.g., GPT-family models, embeddings, RAG pipelines). Demonstrated experience building and deploying machine learning solutions on cloud platforms (AWS, Azure, GCP). Hands-on experience with containerization (Docker, Kubernetes) and CI/CD automation. Deep understanding of data engineering concepts: ETL pipelines, data warehousing, data lakes, databases (SQL/NoSQL), and data integration. Familiarity with structured/unstructured datasets, optimization of data pipelines, and large-scale data storage. Knowledge of agentic development frameworks and agent-based AI systems. Practical experience with AWS AI/ML tools like Amazon SageMaker, Amazon Bedrock, and Lambda for building, fine-tuning, deploying, and managing AI and ML models at scale. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world's toughest problems. You´ll be challenged, but you won't be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next. What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. learn more. Primary country and city: Sweden (SE) || Stockholm Req ID: 771492
