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Tier-1 brand plus broad ML/LLM requirements attract applicants, but seniority reduces mid-level competition.
Core ML/LLM skills transfer across industries, but healthcare experience preference increases domain specificity.
Explicit 10+ years plus many mandatory ML/LLM, deployment, and cloud skills makes filtering stringent.
Lead design and implementation of AI/ML solutions with a focus on enterprise-level agentic solutions and Gen AI applications including fine-tuning LLMs and retrieval-augmented generation (RAG).
Develop and deploy advanced NLP, Document AI, OCR, and vector embedding solutions integrating with cloud platforms (Azure, AWS, GCP).
Own end-to-end delivery by translating business requirements into products with a strong product mindset and problem-solving focus.
Bachelor's degree or higher in Computer Science Engineering.
10+ years of experience in AI/ML roles with hands-on implementation of Gen AI, fine-tuning LLMs, and agentic solutions using frameworks like Langgraph, Google ADK, CrewAI.
Proficiency in Python, PyTorch, SQL, and familiarity with OpenAI API, Streamlit, Flask, FastAPI, Docker, and cloud environments (Azure, AWS, GCP).
Experience implementing machine learning techniques (supervised, unsupervised, reinforcement learning, deep learning, NLP) and deploying LLMs; Healthcare experience preferred but not mandatory.
Experienced AI/ML professional capable of independently delivering complex AI products from business requirements to production deployment.
Strong expertise in modern AI frameworks for agentic systems and LLM fine-tuning, with operational knowledge of vector databases and document extraction.
Comfortable working within healthcare domain challenges, leveraging cloud infrastructure and integrating cutting-edge NLP and Gen AI technologies.