10 - 15 years

60 - 80 Lacs

Posted:1 day ago| Platform: Naukri logo

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Work Mode

Hybrid

Job Type

Full Time

Job Description

Role & responsibilities

  • Expert-level Python for Production Systems: Proven ability to architect and write clean, typed, and testable code for distributed systems.
  • Experience building systems that communicate via message queues like Azure Service Bus.
  • In-depth knowledge of LLM vulnerabilities (OWASP Top 10 for LLMs) and experience implementing guardrails against prompt injections and data
  • Asynchronous & Concurrent Programming: Mastery of Pythons asyncio to build scalable, I/O-bound services
  • Deep, hands-on experience with FastAPI and Pydantic for building robust, schema-validated microservices
  • Cloud-Native Development: Strong experience with Docker for applications and deploying orchestrated environments like Kubernetes (AKS).
  • Responsible for designing, developing, and implementing machine learning models and algorithms to solve complex problems.

Preferred candidate profile

  • LLM & RAG Architecture Expertise
  • Hands-on experience with Retrieval-Augmented Generation (RAG) architectures using embeddings via Azure AI Search and Databricks.
  • Familiar with MCP servers for scalable deployment.s.
  • Familiar with MCP servers for scalable deployment.
  • Agentic AI & Orchestration:
    • Experience with autonomous decision support using LangGraph.
    • Skilled in agent orchestration using Microsoft Copilot Studio and CrewAI.
  • Performance Optimization:
    • Working knowledge of latency reduction techniques for RAG-based applications, including Redis-based caching.
  • LLM Fine-Tuning:
    • Practical understanding of fine-tuning methods such as LoRA (Low-Rank Adaptation).
  • Model Selection & Prompting:
    • Awareness of the latest LLMs tailored to specific use cases (e.g., Claude, Gemini, GPT series).
    • Understanding of prompt engineering requirements across different models.
  • Cost Estimation:
    • Ability to calculate and optimize costs for API-based model usage.

Domain Experience:

  • Understanding of U.S. healthcare ecosystem, and regulatory frameworks (HIPAA, CMS), and reimbursement models.
  • Hands-on expertise in working with clinical OR claims datasets, experience building and deploying ML solutions for prior authorization OR utilization management OR case/care management OR patient engagement OR Claims operations OR Provider operations OR population health analytics OR EHR systems translating domain nuances into feature engineering and evaluation strategies.
  • Quality & outcomes orientation: Familiarity with U.S. quality frameworks (HEDIS, STAR ratings), value-based care initiatives and populationhealth objectives; ability to frame AI/ML work to drive measurable improvements in quality, affordability, and member/provider experience.

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