AI Engineer

Responsibilities:
Design and implement GenAI solutions for medical report understanding and code mapping using LLMs and prompt engineering.
Build and optimise RAG (Retrieval Augmented Generation) systems for accurate and reliable medical coding.
Develop and deploy AI agents for multi-speciality medical coding automation.
Evaluate, benchmark, and select appropriate foundation models (GPT, Claude, Llama, etc. ) for healthcare use cases.
Implement cost-effective, production-ready GenAI architectures with monitoring and observability.
Transform existing rule-based systems into GenAI-powered solutions while maintaining accuracy and compliance.
Collaborate with clinical teams to ensure outputs align with healthcare standards and regulations (HIPAA, ICD-10 CPT, and SNOMED-CT).
Conduct A/B testing, model evaluation, and continuous performance optimisation.
Stay updated with the latest GenAI/LLM research and bring relevant techniques into production.
Requirements:
3+ years of hands-on experience with LLMs/GenAI (GPT, Claude, Llama, PaLM, etc. ).
3+ years overall in data science/ML engineering.
Strong proficiency in Python with GenAI libraries (LangChain, LlamaIndex, Hugging Face, and OpenAI/Anthropic APIs).
Exposure to healthcare NLP (clinical reports, medical coding, and terminologies).
Deep understanding of RAG architectures, embeddings, and vector databases (Pinecone, Weaviate, Chroma).
Production deployment experience: scaling, monitoring, cost optimisation, and MLOps practices.

Experience
3-6 yrs