Description

Key Responsibilities

Data Engineering
· Design and develop scalable data pipelines using Databricks, PySpark, Delta Lake, and Spark SQL.
· Build ELT/ETL solutions for structured and unstructured data.
· Implement Medallion Architecture (Bronze, Silver, Gold layers).
· Optimize Databricks jobs, clusters, and workflows for performance and cost efficiency.
· Manage data ingestion from multiple cloud and on-premise sources.

AI & Machine Learning
· Develop and deploy Machine Learning models using Databricks ML and Python.
· Implement MLOps practices for model training, deployment, monitoring, and governance.
· Build predictive analytics solutions and recommendation engines.
· Create feature stores and reusable ML pipelines.

Generative AI
· Design and develop GenAI applications using:
o Large Language Models (LLMs)
o Retrieval Augmented Generation (RAG)
o Vector Databases
o AI Agents and Multi-Agent Frameworks

Design and develop scalable data pipelines using Databricks.
Implement machine learning models and MLOps practices.
Optimize workflows for performance and cost efficiency.
Collaborate with stakeholders to deliver AI-driven solutions.
Develop generative AI applications and integrate various AI technologies.

Essential Skills:
Experience in healthcare or banking domains is beneficial.
Familiarity with AI governance and agentic AI is a plus.
Knowledge of MLOps practices is advantageous.
Understanding of data modernization programs is helpful.
Certification in Databricks or machine learning is preferred.
Strong expertise in Databricks and data engineering is essential.
Proficiency in machine learning and AI model deployment is critical.
Experience with cloud platforms like Azure or AWS is necessary.
Familiarity with data pipeline design and optimization is required.
Knowledge of generative AI technologies is a must

Key Skills

Education

Any Gradute