Roles & Responsibilities
- Drive development of enterprise-level GenAI applications using LLM frameworks such as Langchain , Autogen , and Hugging Face.
- Architect intelligent pipelines using PySpark , TensorFlow, and PyTorch within Databricks and AWS environments.
- Implement embedding models and manage VectorStores for retrieval-augmented generation (RAG) solutions.
- Integrate and leverage MDM platforms like Informatica and Reltio to supply high-quality structured data to ML systems.
- Utilize SQL and Python for data engineering, data wrangling, and pipeline automation.
- Build scalable APIs and services to serve GenAI models in production.
- Lead cross-functional collaboration with data scientists, engineers, and product teams to scope, design, and deploy AI-powered system
- Ensure model governance, version control, and auditability aligned with regulatory and compliance expectations.
Basic Qualifications and Experience
Master's degree with 8 - 10 years of experience in Data Science, Artificial Intelligence, Computer Science, or related fields ORBachelor's degree with 10 - 14 years of experience in Data Science, Artificial Intelligence, Computer Science, or related fields ORDiploma with 14 - 16 years of hands-on experience in Data Science, AI / ML technologies, or related technical domainsSkills Required
Tensorflow, Pyspark, Databricks, Aws