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Salary
$25k – $71k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior · 5+ years exp
Overview
Company
Impact
Profile match
Arintra is a healthcare technology company based in Austin, Texas, and founded in 2019. The company provides an AI-driven autonomous medical coding and revenue assurance platform that integrates with electronic health record systems to automate insurance claim submissions. It operates across more than 23 medical specialties and multiple care settings, serving large health systems and provider groups throughout the United States.

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:

  • 5+ years of hands-on experience with LLMs/GenAI (GPT, Claude, Llama, PaLM, etc. )
  • 5+ years overall in data science/ML engineering.
  • Strong proficiency in Python with GenAI libraries (LangChain, LlamaIndex, HuggingFace, OpenAI/Anthropic APIs).
  • Deep understanding of RAG architectures, embeddings, and vector databases (Pinecone, Weaviate, Chroma).
  • Production deployment experience: scaling, monitoring, cost optimisation, and MLOps practices.
  • Exposure to healthcare NLP (clinical reports, medical coding, and terminologies).
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