Technical Lead-Data Engg

Birlasoft; Uttar Pradesh; On-site; INR 4,500,000 - 6,500,000; Full time

Job summary

Birlasoft is seeking a Technical Lead-Data Engg to drive GenAI and data engineering initiatives in Noida. You will design AI-enabled solutions, build scalable data pipelines using Databricks and PySpark, and ensure data quality across enterprise systems.

You will lead model development, fine-tuning, and governance, while collaborating with cross-functional teams to deliver cost-efficient, scalable AI-driven data platforms.

Qualifications

• Experience designing GenAI and AI agent solutions with Databricks Genie and Mosaic AI.
• Strong background in building scalable data pipelines (Databricks, PySpark, SQL).
• Hands-on with data quality, observability, and governance (Delta Lake, Unity Catalog).

Responsibilities

• Design and implement GenAI and Agentic AI solutions for enterprise data
• Build LLM-powered use cases and fine-tune models with MLflow
• Develop scalable data pipelines and optimize Spark jobs for cost and performance
• Ingest, transform, and enrich data from multiple sources with quality controls
• Develop enterprise ontologies, taxonomies, and semantic data models
• Prepare feature-ready datasets for AI/ML workloads
• Integrate AI solutions with business workflows and data platforms
• Implement prompt engineering and model evaluation guardrails
• Collaborate with data engineering and AI/ML teams to deliver solutions

Skills

GenAI

Databricks

PySpark

SQL

LLM development

Model training

Mosaic AI

Spark optimization

Data pipelines

MLflow

Education

Bachelor’s degree in CS/CE/IS or equal

Tools

Databricks

Delta Lake

Unity Catalog

Medallion architecture

Job description

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Location: INDIA - NOIDA- BIRLASOFT OFFICE

Title: Technical Lead-Data Engg

Description:

Area(s) of responsibility

Key Responsibilities

- Design and implement GenAI and Agentic AI solutions using Databricks Genie, Databricks Agent Bricks, Mosaic AI (model training, evaluation, prompt engineering, RAG)

- Experience Build LLM-powered use cases

- Design and build scalable data pipelines using Databricks, PySpark, and SQL

- Implement Delta Lake, Unity Catalog, and medallion architecture

- Optimize Spark jobs for performance, cost, and scalability

- Perform data ingestion, transformation, and enrichment from multiple sources

- Ensure data quality, reliability, and observability across pipelines

- Design and develop enterprise ontologies, taxonomies, vocabularies, and semantic data models.

- Support AI/ML workloads by preparing high‑quality, feature‑ready datasets

- Develop and fine-tune LLMs/ML models using Databricks MLflow and Mosaic AI

- Implement prompt engineering, model evaluation, and guardrails for enterprise GenAI use cases

- Integrate AI solutions with enterprise data and business workflows