Lead - Data Engineering
Kyndryl (company site, opens in a new tab)MumbaiOn-siteDirect career pageEst. 22–36 LPA
Posted on 1 Sept
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Job description
Structured overview of role & requirements
About This Role
- Design and scale data pipelines for Retrieval-Augmented Generation (RAG) using unstructured IT logs and documentation to create optimized vector embeddings.
- Manage and ensure performance of vector databases (e.g., Pinecone, Milvus, Weaviate) to deliver sub-second retrieval speeds for AI reasoning processes.
- Develop, deploy, and maintain CI/CD pipelines for data infrastructure, implementing semantic layers, knowledge graphs and automated data guardrails for data quality and safety.
Minimum Requirements
- Expertise in ELK stack (ElasticSearch, Logstash, Kibana).
- Proficiency in Python programming.
- Experience with data mining, data storage, ETL processes, data pipeline development, and tooling.
- Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Strong background in data engineering focusing on AI applications, specifically experience building and scaling AI data infrastructure.
- Experience with both relational (PostgreSQL, DB2) and NoSQL (MongoDB) databases and vector database management.
- Ability to manage multiple projects involving complex data transformation and pipeline orchestration in production environments, with a methodical engineering approach.