Principal Engineer, Data Analytics Engineering (Data Engineering , GenAI , Python , SQL)

Position: Principal AI Engineer (Data Engineering & GenAI) We are seeking a Principal AI Engineer with deep expertise in data engineering, Generative AI (GenAI), and AWS cloud platforms to architect and lead scalable AI-driven solutions that address complex business and technology challenges. Key Responsibilities AI Platform Architecture: Design and implement scalable AI/ML and GenAI solutions on AWS using modern data engineering frameworks and best practices. Data Engineering Leadership: Drive the development of robust data pipelines, ETL/ELT frameworks, and data models that support AI/ML and analytics use cases at scale. Generative AI Solutions: Lead the exploration, prototyping, and deployment of GenAI applications (LLM-based copilots, RAG pipelines, autonomous agents) across enterprise scenarios. Technology Strategy: Evaluate and adopt emerging technologies to strengthen AI and data engineering efficiency and capabilities. Cloud & Infrastructure: Define and implement cloud-native architectures using AWS services (S3, Glue, Redshift, EMR, Lambda, SageMaker, Bedrock, EKS). Collaboration & Influence: Partner with data scientists, product managers, and business stakeholders to translate business needs into scalable technical solutions. Best Practices & Governance: Establish coding standards, DevOps/MLOps practices, and enforce data governance and security controls for AI workloads. Mentorship & Leadership: Guide and mentor engineers and data professionals, fostering innovation, technical excellence, and best practices. Basic Qualifications Bachelor’s or Master’s degree in Computer Science, Data Engineering, AI/ML , or related field. Advance experience in data engineering, AI/ML systems, and cloud computing . Proven expertise with AWS cloud ecosystem (S3, Glue, Redshift, EMR, Lambda, SageMaker, Bedrock) and Databricks . Hands-on experience with Generative AI models (LLMs, diffusion models), frameworks (LangChain, Hugging Face, OpenAI/Anthropic APIs), and RAG implementations. Strong programming skills in Python, SQL , and at least one compiled language ( Java , Scala, or Go). Experience with modern data engineering tools and pipelines (Airflow, Spark, Kafka, dbt, Snowflake). Excellent problem-solving skills with experience in architecture design and stakeholder communication. Preferred Qualifications Experience with vector and graph databases (e.g., Pinecone, Weaviate, pgvector, Neo4j). Working knowledge of MCP (Model Context Protocol) for orchestrating and managing AI agent workflows. Familiarity with containerization and orchestration (Docker, Kubernetes, EKS) Knowledge of MLOps/LLMOps practices and CI/CD pipelines for AI workloads (e.g., MLflow, Kubeflow, LangSmith). Strong understanding of data security, compliance, and governance in cloud and AI systems. Because Western Digital thrives on the power of diversity and is committed to an inclusive environment where every individual can thrive through a sense of belonging, respect, and contribution, we are committed to giving every qualified applicant and employee an equal opportunity.  Western Digital does not discriminate against any applicant or employee based on their protected class status and complies with all federal and state laws against discrimination, harassment, and retaliation, as well as the laws and regulations set forth in the "Equal Employment Opportunity is the Law" poster. WD thrives on the power and potential of diversity. As a global company, we believe the most effective way to embrace the diversity of our customers and communities is to mirror it from within. We believe the fusion of various perspectives results in the best outcomes for our employees, our company, our customers, and the world around us. We are committed to an inclusive environment where every individual can thrive through a sense of belonging, respect and contribution. WD is committed to offering opportunities to applicants with ... (truncated, view full listing at source)