Principal Engineer, Data Analytics Engineering 9+ years (Data Engineering , GenAI , Python , SQL)
Western Digital View all jobs
- Bangalore, Karnataka
- Permanent
- Full-time
- 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.
- 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.
- 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.