We are looking for an experienced AI / LLM Scientist to design, develop, and deploy advanced Generative AI and Large Language Model (LLM) solutions for enterprise-scale healthcare and life sciences applications. The ideal candidate should possess deep expertise in NLP, LLM fine-tuning, prompt engineering, Agentic AI frameworks, and scalable AI solution development.
Key Responsibilities:
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Design, build, and optimize Generative AI and LLM-based applications for healthcare and life sciences use cases.
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Fine-tune, evaluate, and deploy Large Language Models (LLMs) using enterprise datasets.
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Develop and implement NLP pipelines for text processing, summarization, semantic search, and conversational AI systems.
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Build and orchestrate Agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar technologies.
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Perform prompt engineering, prompt optimization, and response evaluation for high-quality AI outputs.
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Work on Retrieval-Augmented Generation (RAG) architectures and vector database integrations.
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Collaborate with cross-functional teams including Data Engineering, Product, and Business stakeholders to deliver scalable AI solutions.
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Develop APIs, microservices, and AI integrations using Python and Java.
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Work with structured and unstructured data using SQL and modern database technologies.
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Conduct model experimentation, performance optimization, and AI solution validation.
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Stay updated with the latest advancements in Generative AI, Agentic AI, LLMOps, and foundation models.
Required Skills:
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10+ years of overall IT experience with strong AI/ML exposure.
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Strong experience in Natural Language Processing (NLP) and Generative AI technologies.
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Hands-on expertise in Fine-Tuning LLMs and Prompt Engineering.
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Experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent.
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Strong programming skills in Python and Java.
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Good understanding of SQL and database concepts.
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Experience with AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, etc.
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Knowledge of RAG pipelines, vector databases, embeddings, and semantic search.
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Exposure to cloud platforms such as AWS, Azure, or GCP.
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Strong analytical, communication, and problem-solving skills.
Preferred Qualifications:
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Degree in Computer Science, Data Science, Artificial Intelligence, or related field.
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Experience working in Healthcare, Pharma, Life Sciences, or Clinical domains is highly preferred.
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Familiarity with Azure OpenAI, Databricks, Neo4j, ChromaDB, Pinecone, or similar tools is an added advantage.
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Experience in scalable AI deployment and MLOps practices is preferred.
