Introduction to NXP

NXP Semiconductors N.V. (NASDAQ: NXPI) enables secure connections and infrastructure for a smarter world, advancing solutions that make lives easier, better, and safer. As the world leader in secure connectivity solutions for embedded applications, NXP is driving innovation in the automotive, industrial & IoT, mobile, and communication infrastructure markets. Built on more than 60 years of combined experience and expertise, the company has over 29,000 employees in more than 30 countries and posted revenue of $8.88 billion.

Introduction to the job

Are you a seasoned Data & AI expert who thrives in solving complex challenges and turning cutting‑edge innovations into real, enterprise‑ready solutions? Within NXP’s global IT organization, you will shape the future of NXP’s R&D data and AI ecosystem—developing architectures, platforms and next‑generation capabilities that empower thousands of engineers, analysts, and business users worldwide.

NXP is rapidly scaling advanced analytics, AI/ML, and data-driven decision making across R&D. As our Data & AI Architect, you will define and realize the architectural vision that underpins these capabilities. You will explore opportunities beyond the paved road, drive innovation, and guide global teams in delivering secure, scalable, and future‑proof data and AI solutions.

This role is ideal for a highly experienced architect ready for their next step—combining deep hands‑on expertise with strategic thinking, leadership, and enterprise collaboration.

Role and responsibilities

• Design and build platform components for an AI platform including core services, SDKs, client libraries, and reusable modules.
• Develop and maintain APIs (REST/gRPC) with clear versioning, backward compatibility, and OpenAPI/IDL specifications.
• Create and support CLIs and developer tools to simplify onboarding, testing, and deployment for internal and external users.
• Integrate features end to end: wire backend services to UI components, ensure consistent UX for platform features, and ship production‑ready front‑end changes.
• Implement SDKs and client libraries across languages, manage packaging, distribution, and semantic versioning.
• Build and operate data pipelines for ingestion, transformation, and feature preparation; ensure data quality, lineage, and monitoring.
• Collaborate with ML and DevOps teams to enable model training, serving, and lifecycle management; integrate with CI/CD and AIOps workflows.
• Ensure reliability and performance: write tests (unit, integration), benchmark services, and optimize latency and throughput.
• Document and evangelize APIs, SDKs, CLIs, and integration patterns; produce examples, tutorials, and SDK reference guides.
• Security and compliance: implement secure defaults, secrets handling, and input validation for APIs and SDKs.
• Continuous improvement: gather developer feedback, iterate on DX (developer experience), and reduce friction for platform consumers.

Education and Experience

• Bachelor’s or master degree in Computer Science, Information Technology, Engineering, or equivalent
• 5+ years of experience in building backend services, SDKs, APIs, and data pipelines; experience with AI/ML platforms or MLOps is highly desirable.
• Proven track record shipping production SDKs or client libraries and maintaining public or internal APIs.
• Experience integrating backend services with modern front ends and shipping UI features.
• Hands‑on experience with data engineering tools and production data workflows.

Required Skills and Expertise

• Languages: Python; plus one or more of Go, Java, or TypeScript.
• API and SDK: REST, gRPC, OpenAPI; SDK design patterns; packaging and distribution (PyPI, npm).
• CLI development: experience building robust CLIs (e.g., Click, argparse, Cobra).
• Frontend integration: familiarity with React, Angular, or Vue for adding UI features and collaborating with frontend engineers.
• Data engineering: SQL, ETL patterns, Spark, Airflow, Kafka or other streaming systems, Parquet/Delta.
• Model serving and MLOps: exposure to model serving frameworks (Triton, TorchServe, KFServing) and model lifecycle concepts.
• Cloud and infra: AWS/GCP/Azure fundamentals; containerization with Docker; Kubernetes basics for deploying services.
• CI/CD and automation: Git, GitOps, Jenkins/GitHub Actions/GitLab CI, automated testing and release pipelines.
• Observability and testing: logging, metrics, tracing (Prometheus, Grafana, OpenTelemetry); strong testing discipline.
• Security and reliability: secrets management, input validation, rate limiting, and API security best practices.
• Soft skills: API design, clear technical writing, cross‑team collaboration, and product mindset.