Shape the future of AI-powered R&D at NXP

Are you ready to build the next generation of data and AI platforms at NXP?

As a Data & AI Engineering Lead, you will play a pivotal role in accelerating and optimizing NXP’s New Product Introductions by designing, implementing, and operationalizing advanced, production-grade data and AI solutions. Your work will empower R&D teams with scalable platforms, intelligent assistants, and automated workflows that turn complex data into actionable insights.

Working closely with data engineers, data scientists, solution architects, and domain experts, you will help define the future of data & AI capabilities within NXP R&D. You will take ownership of delivering robust, secure, and highly available data and AI applications used across the engineering organization.

What you will do as a Data & AI Engineering Lead at NXP

As a senior technical leader within the team, you will shape and evolve the R&D data platform and AI solutions owned by this group. You will explore new ideas, modernize existing capabilities, and drive the platform forward through architectural leadership, hands-on delivery, and close collaboration across disciplines.

Your key responsibilities

• Product Ownership & Backlog Contribution: Support the definition and evolution of the technical roadmap by translating R&D needs into well-scoped, prioritized backlog items, providing technical input to prioritization, and ensuring reliability, security, cost, and operational improvements are explicitly represented.
• Platform Architecture & Strategy: Shape the architecture of the R&D data and AI platform in close collaboration with solution architects, defining technology choices, design patterns, and standards that enable scalable Lakehouse and GenAI workloads with governed self-service, security, and long-term maintainability.
• AI & GenAI Enablement: Design, build, and operationalize production-grade Predictive, Generative and Agentic AI solutions, defining reference architectures for AI workloads and collaborating with data scientists and domain experts to turn experimental use cases into robust, scalable, and secure production systems.
• Large-Scale Platform & AI Initiatives: Lead major initiatives such as lakehouse modernization, migration from AWS Glue to Databricks, and the integration of AI capabilities into existing platforms, enabling reusable data, features, and AI components across R&D.
• Data Engineering & Lakehouse Development: Design and deliver large-scale ETL/ELT pipelines using AWS, Databricks, Delta Lake, and distributed compute patterns, while ensuring consistent schema management, versioning, and data lifecycle practices across structured, semi-structured, and unstructured data.
• AI Operations, Reliability & Cost Control: Implement observability, monitoring, and evaluation for data and AI systems, optimize performance and cost efficiency, and lead incident investigations to drive long-term stability, reliability, and continuous improvement.
• Engineering Excellence & Reusability: Develop reusable frameworks, templates, and libraries for data and AI development, and promote best practices around versioning, evaluation, reproducibility, and lifecycle management.
• Security, Governance & Compliance: Embed strong security, governance, and compliance controls across data and AI solutions, defining access models, data classifications, and responsible AI guardrails in partnership with Cyber Security and Legal teams.
• Leadership & Mentoring: Mentor engineers through architectural guidance, design and code reviews, and elevate team maturity by driving technical standards and a culture of engineering excellence.
• Operational Maturity: Continuously improve operational processes for data and AI platforms by turning production learnings into automation, documentation, and platform enhancements.

What you bring

You can describe yourself as follows:

Education & Experience

• Education: Master’s degree (or equivalent practical experience) in Data Engineering, Computer Science, Software Engineering, or a related technical field.
• Data Engineering Experience: 10+ years of professional experience building and operating large-scale data platforms in enterprise environments with distributed data processing.
• Generative & Agentic AI: 2+ years of hands-on experience designing and delivering Generative and Agentic AI solutions, including concepts such as LLMs, Retrieval-Augmented Generation (RAG), and Model/Agent Control Patterns (e.g. MCP). Experience with Predictive AI is a big plus.
• AWS Data Lake Experience: Extensive experience with AWS-native data lake services, including S3, Glue, Athena, and Lake Formation, covering ETL orchestration, cataloging, governance, retention, aggregation, backfilling, enrichment and secure access management.
• Databricks & Migration Expertise: Deep hands-on experience designing ETL pipelines on Databricks, including proven success migrating existing cloud-native data lakes and workflows to the Databricks platform.
• Delta Lake Expertise: Strong understanding of Delta Lake internals, including schema evolution, time travel, table optimization, and performance tuning at scale.
• High‑Tech Domain Experience (Plus): Background and experience in high‑tech, R&D‑intensive environments, especially within semiconductor or automotive domains, is a strong plus.

Technical Skills

• Architecture & Design: Proven ability to define scalable data architectures, lakehouse patterns, and influence long-term platform strategy.
• Cloud & Automation: Strong experience with cloud-native engineering on AWS. Hands-on experience with Infrastructure-as-Code, CI/CD pipelines, and DevOps / MLOps best practices.
• Programming: Advanced proficiency in Python and SQL, with a focus on building robust, maintainable, and reusable code.
• GenAI Development: Experience working with various large language model families. Hands-on knowledge of RAG pipelines, vector stores, orchestration frameworks, and agent-based architectures. Familiarity with multimodal AI solutions is a strong plus.
• Data Quality & Governance: Strong command of data observability, lineage, metadata management, quality frameworks, and secure access patterns.
• Performance & Cost Optimization: Expertise in cluster and workload tuning, orchestration strategies, storage optimization, and cost management in large-scale data lake and lakehouse environments.

Professional Attributes

• Mentorship & People Development: Proven experience mentoring and guiding engineers, fostering technical excellence, confidence, and continuous growth within the team.
• Technical Leadership: Comfortable guiding engineers at all levels through architectural decisions, code reviews, and best practices.
• Strategic Problem-Solving: Able to own complex technical challenges and design scalable, long-term solutions rather than short-term fixes.
• Stakeholder Mindset: Strong communicator who can translate complex technical concepts into business and R&D value.
• Team-Oriented: A collaborative engineer who raises the overall maturity of the team and contributes to a constructive, inclusive engineering culture.
• Agile Ways of Working: Champions Agile and Scrum ways of working, enabling iterative delivery, effective backlog refinement, sprint planning, and strong cross functional collaboration.

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