Job
Description
GenAI & LLM Solution Development : Lead the design and development of enterprise-level GenAI applications using LLM frameworks like Langchain, Autogen, and Hugging Face. AI/ML Pipeline Architecture : Architect AI pipelines using PySpark, TensorFlow, PyTorch in Databricks and AWS, with a focus on scalable GenAI and data science solutions. RAG & Vector DB Expertise : Implement embedding models and manage VectorStores for retrieval-augmented generation (RAG) use cases. MDM Integration : Integrate and use MDM platforms (e.g., Informatica, Reltio) to provide high-quality, structured data for ML models. Data Engineering & Automation : Use Python and SQL for data wrangling, pipeline automation, and building APIs for deploying GenAI models. Cross-Functional Collaboration : Work with data scientists, engineers, and product managers to deliver AI-powered systems aligned with business goals. Governance & Compliance : Ensure GenAI models follow governance standards, version control, and are audit-ready per regulatory needs. Extensive Technical Skills : Deep hands-on experience with LLMs, PyTorch, Langchain, SK-learn, Hugging Face, TensorFlow, AWS, MongoDB, and Databricks. Preferred Experience : Knowledge of life sciences domain, data modeling, ETL, Agile tools (JIRA, Confluence), and scalable architecture practices. Soft Skills & Leadership : Strong communication, mentoring, analytical, and collaboration skills with the ability to lead virtual global teams and provide thought leadership.
