The job in short
AI data engineering.
SparkSnowflakeDatabricksAWSAzureGCPMachine learningGenAIGenerative AILLMLLMsRAG
Details
- Found
- 14 d ago · posted 2026-10-01
- Location as listed
- Noida, India
- Role
- Data Engineer
- Source
- The company’s own careers site
Full description
About the role
We are seeking a highly skilled AI Data Engineer to design, build, and optimize scalable AI/ML data platforms and pipelines supporting Generative AI, Large Language Models (LLMs), and advanced analytics solutions. The ideal candidate will have hands-on experience with Databricks (DBRX ecosystem), cloud platforms (AWS, Azure, or GCP), modern data engineering frameworks, and Snowflake data warehousing solutions.
Responsibilities
Data Engineering & AI Platform Development
Design and develop scalable batch and real-time data pipelines using Databricks, Spark, and cloud-native services.
Build and maintain enterprise-grade data lakes, lakehouses, and data warehouses.
Create ingestion frameworks for structured, semi-structured, and unstructured data.
Develop AI-ready data pipelines supporting LLM, RAG, Agentic AI, and predictive analytics use cases.
Implement metadata management, data governance, lineage, and data quality controls.
AI & Generative AI Enablement
Prepare and transform data for LLM training, fine-tuning, and inference workloads.
Build Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and enterprise knowledge sources.
Develop data workflows supporting DBRX and other foundation models.
Optimize AI data pipelines for performance, scalability, and cost efficiency.
Cloud & Data Platform Engineering
Design solutions on AWS, Azure, or GCP environments.
Leverage cloud-native data services for ingestion, storage, orchestration, and monitoring.
Implement CI/CD and Infrastructure as Code (IaC) practices for data platforms.
Ensure security, compliance, and governance across cloud data ecosystems.
Technical requirements
Experience building enterprise GenAI or AI Agent solutions.
Exposure to DBRX foundation models and Databricks AI platform.
Hands-on experience with vector search and semantic retrieval systems.
Knowledge of DataOps, MLOps, and LLMOps best practices.
Experience with multi-cloud data architectures.
Understanding of Responsible AI and model governance frameworks.
Preferred skills
Technology->AI-Data science->Machine Learning,Technology->AI-Generative AI->Artificial Intelligence - BASIC,Technology->AI-Generative AI->Generative AI for Data Analytics
Education
Bachelor of Engineering,Bachelor Of Technology (Integrated)
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