Staff AI Data Engineer

About Analog Devices

Analog Devices, Inc. (NASDAQ: ADI) is a global leader in semiconductors that integrates analog, digital, AI, and software technologies to advance automation, robotics, healthcare, energy, and mobility sectors. With over $12 billion revenue in FY22 and a workforce of around 25,000, ADI enables innovators to push boundaries with solutions spanning intelligent edge applications.

Role Overview

The Staff AI Data Engineer role involves leading the design and implementation of cutting-edge, scalable, secure data and AI solutions at ADI. This position requires expertise grounded in distributed systems, data modeling, and machine learning fundamentals—not just tools alone. You'll oversee end-to-end delivery from architecture to deployment, mentor teams, translate complex problems into actionable data strategies, and maintain a long-term focus on quality, reliability, and operations such as DataOps, MLOps, and LLMOps.

Responsibilities

• Design and implement comprehensive data and AI pipelines spanning ingestion, curation, modeling, serving, and consumption with scalable and production-ready standards.
• Construct modern data platforms targeting lakehouse and cloud warehouse architectures using streaming and batch processes, medallion designs, data mesh principles, and governance methods.
• Develop and deploy AI and generative AI solutions including retrieval-augmented generation (RAG), large language model-powered agents, vector searches, and machine learning techniques like anomaly detection, managing their full production life cycle.
• Innovate AI-assisted engineering workflows by orchestrating LLM-agent automations to speed up data pipeline creation, code review, testing, deployment, and operational monitoring.
• Establish robust MLOps/LLMOps infrastructures encompassing experiment tracking, model evaluation, serving, guarding costs, and ensuring quality for AI workloads.
• Lead expansive data and analytics projects, collaborating with cross-functional teams, scoping business challenges, identifying essential data, and directing contributors.
• Collaborate with platform, infrastructure, and architecture teams to ensure cohesive, resilient, high-quality enterprise data feeds and system alignment.
• Drive innovation by designing new frameworks, standards, reference architectures, prototypes, and automation tools focused on AI and DataOps enhancement.
• Advise senior stakeholders by delivering clear data-driven insights and fostering consensus around data and AI-driven transformation initiatives.
• Build and maintain scalable processing systems capable of handling petabyte-scale structured, semi-structured, and unstructured data for analytics and actionable insight generation.
• Create interactive dashboards and data products using tools like Power BI and Tableau.

Technology & Tools

• Cloud platforms: AWS and Azure including compute, storage, networking, identity, and cost considerations.
• Data platforms: Databricks with Spark, Delta Lake, Structured Streaming, Auto Loader, Unity Catalog; Snowflake; lakehouse and warehouse system design.
• Programming and data processing: Advanced Python, SQL, Apache Spark for batch and streaming workloads.
• Data engineering: Use of dbt, medallion and data mesh patterns, orchestration tools like Databricks Jobs, Airflow, Dagster, CDC and streaming via Kafka and Auto Loader.
• AI/ML and GenAI: Practical experience with LLMs including RAG, agentic workflows, vector search, prompt engineering, time-series anomaly ML, MLflow or similar for tracking/evaluation/model serving, and LLMOps/MLOps practices.
• AI-assisted engineering: Experience with LLM-agent orchestration to automate engineering workflows is highly valued.
• Business Intelligence: Power BI preferred, familiarity with building data APIs and consumable data assets.
• Engineering methods: Git workflows, CI/CD, containerization (Docker), testing, observability, DataOps, and reproducible engineering approaches.
• Bonus: Knowledge of telemetry and columnar data storage, data modeling tools, and data governance platforms.

Qualifications

• 10+ years of professional experience in software and data engineering, including at least 5 years specifically in data engineering and delivery of AI/ML or generative AI production systems.
• A relevant degree in Computer Science, Electrical or Computer Engineering, Data Science, or related technical disciplines; advanced degrees are an advantage.
• Expertise in distributed and streaming systems fundamentals, data engineering (Spark, Kafka, Delta Lake), and orchestration for scalable pipelines.
• Strong command of Python, SQL, database design, data modeling, and master data management strategies.
• Proven record in architecting and owning data products and AI solutions throughout their end-to-end lifecycle.
• Hands-on knowledge of applying AI to enterprise data including LLMs, RAG, vector search, evaluation metrics, guardrails, and cost-quality management.
• Experience mentoring and leading technical teams and integrating modern development tools and software engineering best practices.
• Excellent communicator able to influence peers and leadership, lead cross-functional teams, and engage business stakeholders effectively.

Additional Preferred Qualifications

• Work experience in the semiconductor manufacturing sector with knowledge of design and manufacturing data domains.
• Experience working in enterprise data mesh environments involving multiple source systems like ERP, CRM, and telemetry.
• Competence in managing AI/GenAI workload usage, cost, and quality governance.
• Relevant certifications in cloud, Databricks, Snowflake, or data/AI engineering disciplines.

Other Information

Applicants except for US citizens, permanent residents, or certain protected groups may require export licensing approval due to access to technical data governed by U.S. regulatory bodies.

This role requires up to 10% travel and operates during standard first shift/daytime hours.

Analog Devices is committed to equal opportunity employment and supports a diverse and inclusive workplace where all individuals have equal opportunity to succeed regardless of personal attributes or affiliations.