Drive development of enterprise-levelGenAIapplications using LLM frameworks such asLangchain,Autogen, and Hugging Face. Architect intelligent pipelines usingPySpark, TensorFlow, andPyTorchwithin Databricks and AWS environments. Implement embedding models andmanageVectorStoresfor retrieval-augmented generation (RAG) solutions. Integrate andleverageMDM 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 serveGenAImodels in production. Lead cross-functional collaboration with data scientists, engineers, and product teams to scope, design, and deploy AI-powered systems. Ensure model governance, version control, and auditability aligned with regulatory and compliance expectations. Basic Qualifications and Experience: Master s degree with8-10years of experience in Data Science, Artificial Intelligence, Computer Science, or related fields OR Bachelor s degree with 10 - 14 years of experience in Data Science, Artificial Intelligence, Computer Science, or related fields OR Diploma with 14 - 16 years of hands-on experience in Data Science, AI/ML technologies, or related technical domain Functional Skills: Must-Have Skills: 10+ years of experience working in AI/ML or Data Science roles, includingdesigningand implementingGenAIsolutions. Extensive hands-on experience with LLM frameworks and tools such asLangchain,Autogen, Hugging Face, OpenAI APIs, and embedding models. Strong programming background with Python,PySpark, and experience in building scalable solutions using TensorFlow,PyTorch, and SK-Learn. Proventrack recordof building and deploying AI/ML applications in cloud environments such as AWS. Expertisein developing APIs, automation pipelines, and servingGenAImodels using frameworks like Django,FastAPI, andDataBricks. Solid experience integrating and managing MDM tools (Informatica/Reltio) and applying data governance best practices. Guide the team on development activities and lead the solution discussions Must have core technical capabilities inGenAI, Data Science space Good-to-Have Skills: Prior experience in Data Modeling, ETL development, and data profiling to support AI/ML workflows. Working knowledge of Life Sciences or Pharma industry standards and regulatory considerations. Proficiencyin tools like JIRA and Confluence for Agile delivery and project collaboration. Familiarity with MongoDB,VectorStores, and modern architecture principles for scalableGenAIapplications. Professional Certifications: Any ETL certification (e.g.Informatica) Any Data Analysis certification (SQL) Any cloud certification (AWS or AZURE) Data Science and ML Certification Soft Skills: Strong analytical abilities to assess and improve master data processes and solutions. Excellent verbal and written communication skills, with the ability to convey complex data concepts clearly to technical and non-technical stakeholders. Effective problem-solving skills to address data-related issues and implement scalable solutions. Ability to work effectively with global, virtual teams