AI/ML Lead - Healthcare Domain

About the Role:

As the AI/ML Lead, you will spearhead the design, development, and production deployment of AI-powered solutions across our healthcare ecosystem. You will drive the company's AI strategy by building scalable Machine Learning, Generative AI, and Agentic AI architectures while collaborating closely with Engineering, Product, and Business teams. You will own the complete AI lifecycle from rapid experimentation to resilient production deployment, transforming raw clinical and handwritten data into structured healthcare intelligence.

The Knockout Criteria (Non-Negotiables):

• Experience & Lifecycle Ownership: 5 - 7 years of hands-on experience building, scaling, and deploying AI/ML models into high-availability production environments.

• Generative AI & Agentic Systems: Deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Multi-Agent Systems, Prompt Engineering, Model Fine-Tuning, Model Evaluation, and Model Context Protocol (MCP).

• AI/ML Frameworks & Libraries: Expert proficiency in Python, PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, LangChain, LangGraph, LlamaIndex, FastAPI, Pandas, and NumPy.

• Model Serving & LLM APIs: Hands-on integration with OpenAI APIs, Anthropic Claude, Google Gemini, Ollama, and vLLM.

• Vector & Traditional Databases: Experience with Vector DBs (Pinecone, Milvus, Weaviate, ChromaDB, FAISS, pgvector) alongside PostgreSQL, MongoDB, Redis, and Kafka.

• Production MLOps & Cloud: Hands-on deployment with Docker, Kubernetes, AWS (SageMaker, Bedrock, EC2, S3, Lambda), MLflow, GitHub Actions, CI/CD, and Observability (ELK, Prometheus, Grafana, OpenTelemetry).

• Domain Preference: Prior experience in Healthcare, Pharma, Clinical Research, Digital Health, EMR/EHR, Medical Imaging, OCR, or Intelligent Document Processing (IDP). Working knowledge of HL7, FHIR, and HIPAA compliance is a strong advantage.

Key Responsibilities:1. AI Strategy, Generative AI & Agentic Architecture:

• Architect and deploy production-grade GenAI, LLM, and Agentic AI solutions to automate healthcare data processing and clinical insights.

• Design robust RAG pipelines, semantic search architectures, and multi-agent workflows with vector embeddings.

• Drive proof-of-concepts (PoCs) from initial research into scalable, high-throughput production services.

2. Computer Vision, Document Processing & Model Fine-Tuning:

• Build and refine Intelligent Document Processing (IDP) and OCR pipelines to extract clinical data from handwritten medical prescriptions with high accuracy.

• Fine-tune, evaluate, and benchmark foundation models on domain-specific clinical datasets.