IND Senior Engineer, Data

About this role

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Skills you'll use

What you'll do

• Data and AI Engineering lead for large and complex data ecosystem leveraging data domains, data products, cloud and modern technology stack
• Real-Time Data Streaming: Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar.
• Data and AI Engineering lead responsible for Implementing Data and AI pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions. This Includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready.
• Develop Data and AI-driven systems to improve data capabilities, ensuring compliance with industry best practices.
• Develop data domains and data products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics etc.
• Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure.
• Collaborate with cross-functional teams to integrate solutions into operational processes and systems supporting various functions.
• Stay up to date with industry advancements in GenAI and apply modern technologies and methodologies to our systems. This includes designing prototypes (POCs) and conduct experiments, and recommend innovative tools and technologies to enhance data capabilities enabling business strategy.
• Model domain entities, relationships, and business logic in knowledge graphs (e.g., Neo4j, Amazon Neptune, RDF). Integrate data from multiple sources, ensuring canonical representation and semantic consistency.
• Synthetic data generation: Develop and validate synthetic data to simulate rare events and edge cases, supporting robust agent evaluation. Integrate synthetic data workflows with automated testing frameworks to ensure consistent, scalable agent performance assessment.

Who can apply

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field.
• Data engineering experience including Data solutions, SQL and NoSQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE), Python/Spark, Datamesh, Datalake or Data Fabric.
• Mastery level data engineering and architecture skills, including deep expertise in data architecture patterns, data warehouse, data integration, data lakes, data domains, data products, business intelligence, and cloud technology capabilities.
• Expertise with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
• Data engineering experience focused on supporting Generative AI technologies.
• Hands on experience with Snowflake
• Experience with building Data and AI pipelines that bring together structured, semi-structured and unstructured data. This includes pre-processing with extraction, chunking, embedding and grounding strategies, semantic modeling, and getting the data ready for Models and Agentic solutions.
• Strong hands-on experience implementing production ready enterprise grade GenAI data solutions.
• Experience with prompt engineering techniques for large language models.
• Experience in implementing Retrieval-Augmented Generation (RAG) pipelines, integrating retrieval mechanisms with language models.

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