AI/LLM Scientist

Description :

Key Responsibilities :

- Fine-tune, evaluate, and deploy Large Language Models (LLMs) using enterprise datasets.

- Develop and implement NLP pipelines for text processing, summarization, semantic search, and conversational AI systems.

- Build and orchestrate Agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar technologies.

- Perform prompt engineering, prompt optimization, and response evaluation for high-quality AI outputs.

- Work on Retrieval-Augmented Generation (RAG) architectures and vector database integrations.

- Collaborate with cross-functional teams including Data Engineering, Product, and Business stakeholders to deliver scalable AI solutions.

- Develop APIs, microservices, and AI integrations using Python and Java.

- Work with structured and unstructured data using SQL and modern database technologies.

- Conduct model experimentation, performance optimization, and AI solution validation.

- Stay updated with the latest advancements in Generative AI, Agentic AI, LLMOps, and foundation models.

Required Skills :

- Strong experience in Natural Language Processing (NLP) and Generative AI technologies.

- Hands-on expertise in Fine-Tuning LLMs and Prompt Engineering.

- Experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent.

- Strong programming skills in Python and Java.

- Good understanding of SQL and database concepts.

- Experience with AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, etc.

- Knowledge of RAG pipelines, vector databases, embeddings, and semantic search.

- Exposure to cloud platforms such as AWS, Azure, or GCP.

- Strong analytical, communication, and problem-solving skills.

Preferred Qualifications :

- Experience working in Healthcare, Pharma, Life Sciences, or Clinical domains is highly preferred.

- Familiarity with Azure OpenAI, Databricks, Neo4j, ChromaDB, Pinecone, or similar tools is an added advantage.

- Experience in scalable AI deployment and MLOps practices is preferred.