Staff AI Data Engineer

About Analog Devices Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, AI, and software technologies into solutions that combat climate change, reliably connect humans and the world, and help drive advancements in automation and robotics, mobility, healthcare, energy and data centers. With revenue of more than $11 billion in FY25, ADI ensures today's innovators stay Ahead of What's Possible. Learn more at www.analog.com and on LinkedIn and X . Staff AI Data Engineer Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $12 billion in FY22 and approximately 25,000 people globally working alongside 125,000 global customers, ADI ensures today’s innovators stay Ahead of What’s Possible. Overview As a Staff AI Data Engineer, you will provide technical leadership and hands-on delivery for ADI’s next generation of data and AI solutions. You operate from first principles — reasoning from the fundamentals of distributed systems, data modeling, and machine learning rather than from tooling alone — to design, build, and deploy end-to-end solutions that are highly scalable, performant, secure, and resilient. You are equally strong in advanced data engineering and in applied AI: you can architect a petabyte-scale lakehouse and, in the same breath, design and ship a production AI/GenAI solution on top of it. You plan, design, implement, deploy, and operate; you guide and mentor engineers; and you translate ambiguous business problems into data-driven outcomes with senior stakeholders. You bring strategic, long-term focus on code quality, reliability, reusability, maintainability, DataOps, and MLOps/LLMOps. Responsibilities • Architect and deliver end-to-end data and AI solutions — from ingestion and curation to modeling, serving, and consumption — that are scalable, secure, and production-grade. • Design and build modern data platforms on the lakehouse and cloud data warehouse: streaming and batch ingestion, medallion (Bronze/Silver/Gold) architectures, data mesh patterns, data modeling, data quality, lineage, security, and governance. • Design, implement, and deploy AI/GenAI solutions on enterprise data — including retrieval-augmented generation (RAG), LLM-powered agents and agentic workflows, vector search, and classical/statistical ML (e.g., anomaly and excursion detection) — and take them through their full lifecycle from prototype to production. • Build and champion AI-assisted engineering and agentic DataOps — designing LLM-agent orchestration and automation that accelerate data pipeline development, code review, testing, deployment, and operations across the team. • Stand up MLOps/LLMOps foundations: experiment tracking, model/prompt evaluation, model serving, monitoring, evaluation harnesses, cost and quality guardrails for AI workloads. • Lead the execution of large-scale, complex data and analytics efforts; scope key business challenges, identify the right data, and provide direction to data analysts, data scientists, data engineers, product managers, and business stakeholders. • Partner with data platform, infrastructure, and enterprise architecture teams to architect and connect high-quality, resilient data feeds across the enterprise, and ensure architectural alignment. • Drive innovation by creating new frameworks, standards, reference architectures, prototypes, and automation — including AI-assisted engineering and DataOps tooling. • Advise and influence business leaders and senior stakeholders with data-driven insights; communicate both the high-level concept and detailed user stories, and build consensus to adopt data- and AI-driven improvements. • Design, build, and maintain robust processing frameworks for petabyte-scale structured, semi-structured, and unstructured data to enable actionable insights and analytics. • Build dashboards and data products using enterprise BI tools such as Power BI (preferred) and Tableau. Technology & Tools You bring first-principles, hands-on depth across the following. Breadth is expected at this level; not every item is mandatory, but the candidate should be strongly grounded in the core stack. • Cloud: Cloud platforms — AWS and Azure (compute, storage, networking, identity, cost). • Data platforms: Databricks (Spark, Delta Lake, Structured Streaming, Auto Loader, Unity Catalog), Snowflake, and lakehouse/warehouse design. • Languages & processing: Expert-level Python and SQL; strong Apache Spark (batch and streaming). • Data engineering: dbt, medallion and data-mesh architectures, orchestration (Databricks Jobs/Asset Bundles, Airflow/Dagster), CDC and streaming (Kafka/Auto Loader). • AI/ML & GenAI: Applied LLMs and GenAI — RAG, agents/agentic orchestration, vector search, prompt and context engineering; ML for time-series/anomaly detection; MLflow or equivalent for tracking, evaluation, and model serving; LLMOps/MLOps. • AI-assisted engineering: Experience building or operating LLM-agent orchestration (e.g., Claude/Anthropic, agent SDKs) to automate data engineering and DataOps workflows is a strong differentiator. • BI & consumption: Power BI (preferred), building consumable data products/APIs. • Engineering practices: Git-based workflows, CI/CD, containerization (Docker), testing, and observability; DataOps and reproducible engineering. • Nice to have: Familiarity with high-volume telemetry/columnar stores, data modeling tools, and data governance tooling is a plus. Qualifications • 10+ years of experience in software/data engineering, including