Principal Engineer, Data Analytics Engineering at Western Digital

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

29 Dec, 25

Salary

0.0

Posted On

30 Sep, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Generative AI, AWS, AI Solutions, Data Pipelines, ETL, Cloud Architecture, Python, SQL, DevOps, MLOps, Data Governance, Mentorship, Problem Solving, Stakeholder Communication, Data Security

Industry

Computer Hardware Manufacturing

Description

Company Description The future. It’s on you. You & Western Digital. We’ve been storing the world’s data for more than 50 years. Once, it was the most important thing we could do for data. Now we’re helping the world capture, preserve, access and transform data in a way only we can. The most game-changing companies, consumers, professionals, and governments come to us for the technologies and solutions they need to capture, preserve, access, and transform their data. But we can’t do it alone. Today’s exceptional data challenges require your exceptional skills. It’s You & Us. Together, we’re the next big thing in data. Western Digital® data-centric solutions are found under the G-Technology™, HGST, SanDisk®, Tegile™, Upthere™, and WD® brands. Job Description 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. Qualifications Basic Qualifications Bachelor’s or Master’s degree in Computer Science, Data Engineering, AI/ML, or related field. 9+ years of 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. Additional Information 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. Western Digital 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. Western Digital is committed to offering opportunities to applicants with disabilities and ensuring all candidates can successfully navigate our careers website and our hiring process. Please contact us at [email protected] to advise us of your accommodation request. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying. Notice To Candidates: Please be aware that Western Digital and its subsidiaries will never request payment as a condition for applying for a position or receiving an offer of employment. Should you encounter any such requests, please report it immediately to Western Digital Ethics Helpline or email [email protected].

Responsibilities

The Principal AI Engineer will design and implement scalable AI/ML and GenAI solutions on AWS, leading the development of robust data pipelines and frameworks. They will also explore and deploy Generative AI applications across enterprise scenarios while collaborating with various stakeholders.