AI Engineer
Myntra · Bengaluru
- Experience6–10 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelsenior
- Posted2 Sept 2026
About Myntra
Myntra is hiring in Bengaluru in ecommerce retail. This role looks for around 6+ years of experience.
Skills
- Generative AI
- large language models
- prompt engineering
- retrieval-augmented generation
- Python
- LangChain
- LlamaIndex
- Hugging Face
- OpenAI APIs
- Anthropic APIs
- healthcare NLP
- medical coding
- RAG architectures
- embeddings
- Pinecone
- Weaviate
- Chroma
- MLOps
- HIPAA
- ICD-10
- CPT
- SNOMED-CT
The role
A generative AI engineer at a healthcare technology company designs medical coding automation with generative AI, retrieval-augmented generation, and healthcare NLP, then deploys production systems with Python and MLOps.
Full job description
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, SNOMED-CT).
Conduct A/B testing, model evaluation, and continuous performance optimisation.
Requirements:
3+ years of hands-on experience with LLMs/GenAI (GPT, Claude, Llama, PaLM, etc. )
6+ years overall in Data Science/ML Engineering.
Strong proficiency in Python with GenAI libraries (LangChain, LlamaIndex, HuggingFace, OpenAI/Anthropic APIs).
Exposure to healthcare NLP (clinical reports, medical coding, terminologies).
Deep understanding of RAG architectures, embeddings, and vector databases (Pinecone, Weaviate, Chroma).
Production deployment experience: scaling, monitoring, cost optimisation, and MLOps practices.