ML OPS Engineer - RandD IT

Are you ready to join the future of innovation at NXP? As an MLOps Engineer with a focus on Data & Machine Learning, you will accelerate NXP’s New Product Introductions by building reliable, scalable, and automated infrastructure that powers analytics and ML solutions across R&D. Your work enables rapid experimentation, seamless deployments, and robust production operations for data‑driven applications and machine‑learning models. You will collaborate closely with data scientists, data engineers, software developers, and IT teams to advance modern DevOps and MLOps practices. This role offers the opportunity to introduce new technologies, shape platform standards, and drive continuous improvement across our R&D analytics ecosystem. This is what you will do as MLOps Engineer at NXP As part of the Hardware Design Analytics team, you will develop and maintain the infrastructure and operational capabilities behind our global R&D analytics environment. You’ll play a key role in enhancing performance, reliability, and scalability while contributing to a culture built on collaboration, experimentation, and continual learning. Your key responsibilities[FP1.1][FP1.2][FP1.3][FP1.4][FP1.5]

• Stakeholder Collaboration: Work with project managers, resource managers, IT teams, and other stakeholders to gather requirements, define project scope, and ensure alignment with business objectives.
• CI/CD, Automation & Developer Experience: Design and maintain automated pipelines and development tooling that streamline the workflow for data scientists and ML engineers. Provide standardized environments, reusable templates, and smooth local‑to‑production processes to improve productivity and ensure fast, reliable delivery across ML, analytics, and data engineering projects.
• Platform & Infrastructure Engineering: Develop and manage cloud and on‑prem infrastructure supporting data processing, analytics applications, and ML workloads. Ensure reliability, scalability, and reproducibility.
• MLOps & Model Lifecycle Support: Support both existing ML models already running in production and the development of future AI/ML products. Implement and maintain model registries, deployment workflows, monitoring solutions, and automated retraining strategies to ensure reliable, long‑term model operations.
• GenAI Platform Enablement: Build and operate infrastructure for Generative AI applications—such as setting up and maintaining MCP servers for internal chatbots and knowledge assistants. Support existing GenAI products already in production and ensure they run securely, efficiently, and at scale.
• Data & Analytics Pipeline Enablement: Partner with data engineers to enhance data pipelines, ensure data quality, and optimize workflows powering visualizations, dashboards, and ML systems.
• Cross‑functional Collaboration: Work with teams across R&D, IT, and product areas to gather requirements, co‑design solutions, and align infrastructure decisions with business needs. What you bring[FP2.1] You can describe yourself as follows: Education & Experience
• Education: Master’s degree in data engineering, Software Engineering, Computer Science, or a related technical field
• Experience: 10+ years of experience as a software, data or DevOps engineer, preferably within a complex IT or R&D environment Technical Skills
• Strong proficiency in Python and Bash
• Hands‑on experience with containerization (Docker)
• Experience implementing monitoring and observability solutions – ideally Splunk, but others are welcome (Prometheus, Grafana, ELK)
• Proficiency with Git and experience working with modern version‑control platforms – preferably GitLab
• Experience building and maintaining cloud infrastructure, ideally on AWS
• Proven experience writing Infrastructure as Code (IaC) using tools such as Terraform or Cloud Development Kit (CDK) Professional Attributes
• Strategic Problem-Solving: Comfortable owning technical challenges and designing long-term, scalable solutions.
• Customer & Stakeholder Focus: Strong communicator who can translate technical concepts into business value and collaborate effectively across data science, architecture, and wider R&D.
• Team Mindset: A natural collaborator who contributes to an open, supportive working culture.
• Agile & Scrum: Experienced working in Agile environments, actively participating in sprints, stand-ups, and iterative delivery cycles to ensure continuous improvement and timely value delivery.