ML OPS Engineer R&D 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 NXPs 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 datadriven applications and machinelearning 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 NXPAs part of the Hardware Design Analytics team you will develop and maintain the infrastructure and operational capabilities behind our global R&D analytics environment. Youll play a key role in enhancing performance reliability and scalability while contributing to a culture built on collaboration experimentation and continual key responsibilitiesFP1.1FP1.2FP1.3FP1.4FP1.5Stakeholder Collaboration: Work with project managers resource managers IT teams and other stakeholders to gather requirements define project scope and ensure alignment with business 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 localtoproduction processes to improve productivity and ensure fast reliable delivery across ML analytics and data engineering & Infrastructure Engineering: Develop and manage cloud and onprem infrastructure supporting data processing analytics applications and ML workloads. Ensure reliability scalability and & 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 longterm model Platform Enablement: Build and operate infrastructure for Generative AI applicationssuch 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 & Analytics Pipeline Enablement: Partner with data engineers to enhance data pipelines ensure data quality and optimize workflows powering visualizations dashboards and ML Collaboration: Work with teams across R&D IT and product areas to gather requirements codesign solutions and align infrastructure decisions with business you bringFP2.1You can describe yourself as follows: Education & ExperienceEducation: Masters degree in data engineering Software Engineering Computer Science or a related technical fieldExperience: 10 years of experience as a software data or DevOps engineer preferably within a complex IT or R&D environmentTechnical SkillsStrong proficiency in Python and BashHandson 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 versioncontrol platforms preferably GitLabExperience building and maintaining cloud infrastructure ideally on AWSProven experience writing Infrastructure as Code (IaC) using tools such as Terraform or Cloud Development Kit (CDK)Professional AttributesStrategic Problem-Solving: Comfortable owning technical challenges and designing long-term scalable & Stakeholder Focus: Strong communicator who can translate technical concepts into business value and collaborate effectively across data science architecture and wider R& Mindset: A natural collaborator who contributes to an open supportive working & Scrum: Experienced working in Agile environments actively participating in sprints stand-ups and iterative delivery cycles to ensure continuous improvement and timely value delivery.

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