Urgent Requirement AI ML Developer Healthcare AI

Role Overview

We are looking for an AI/ML Developer to join our healthcare AI initiative and work on the design, development, experimentation, evaluation, and deployment of AI/ML and Generative AI solutions for healthcare. This role is specifically focused on AI/ML engineering, rather than conventional web or full-stack development. The candidate will work closely with the existing full-stack development team to build AI capabilities that can be integrated into hospital applications, workflows, dashboards, and patient/clinical systems. The ideal candidate should have hands-on experience with LLMs, RAG, model fine-tuning, LoRA/QLoRA, embeddings, vector databases, prompt engineering, model evaluation, and AI inference/deployment.

Key Responsibilities

1. Generative AI & LLM Development

• Design and develop LLM-powered applications for healthcare use cases. Work with commercial and open-source LLMs. Evaluate different models for accuracy, latency, cost, multilingual capabilities, and healthcare use cases. Develop AI agents, assistants, conversational systems, and domain-specific AI applications. Implement structured prompting, function/tool calling, structured outputs, and agentic workflows. Experiment with different model architectures and inference approaches.

1. LLM Fine-Tuning

Hands-on experience in fine-tuning LLMs is strongly preferred. The candidate should have practical experience with: LoRA (Low-Rank Adaptation) QLoRA Parameter-efficient fine-tuning (PEFT) Supervised Fine-Tuning (SFT) Instruction tuning Dataset preparation and cleaning Training/validation dataset creation Model checkpointing and evaluation Quantization Hugging Face Transformers ecosystem Experience fine-tuning models such as Llama, Qwen, Mistral, Gemma, Phi, or equivalent open-source models is desirable. Candidates should be able to demonstrate at least one hands-on LLM fine-tuning project, rather than only theoretical knowledge.

1. RAG & Knowledge Systems

Design and implement production-quality Retrieval-Augmented Generation (RAG) systems. Experience with: Document ingestion and preprocessing Chunking strategies Embeddings Vector databases Semantic search Hybrid search Metadata filtering Reranking Retrieval evaluation Context management Hallucination reduction Citation/grounding mechanisms Experience with technologies such as FAISS, Qdrant, Weaviate, Milvus, pgvector, Chroma, Elasticsearch/OpenSearch, or equivalent is desirable.

1. AI Agents & Tool Calling

Develop AI systems capable of interacting with external tools and hospital systems. Experience with: Function/tool calling API integration Agent workflows Multi-step reasoning workflows Structured JSON outputs Workflow orchestration State/session management Guardrails Human-in-the-loop workflows Examples include AI systems capable of interacting with hospital APIs, retrieving patient information, checking appointments, generating reports, or assisting hospital staff. 5. Machine Learning

Strong understanding of fundamental ML concepts including: Supervised and unsupervised learning Classification Regression Clustering Feature engineering Model training and validation Model evaluation Cross-validation Overfitting/underfitting Precision, recall, F1, ROC-AUC and other relevant metrics Experience with scikit-learn, XGBoost, PyTorch and/or TensorFlow is desirable.

1. AI Evaluation & Quality

Develop systematic methods to evaluate AI systems before and after deployment. Work on: Accuracy evaluation Hallucination measurement Retrieval evaluation LLM-as-a-judge approaches Benchmark datasets Prompt/model comparison Regression testing for AI responses Latency and cost measurement Safety and reliability testing Multilingual evaluation The candidate should understand that AI development requires continuous evaluation and experimentation, not simply connecting an API to an application.

1. Healthcare AI

Work with clinical and operational teams to identify and develop AI applications across areas such as: Clinical decision support Patient communication Hospital chatbots Voice AI Medical information retrieval Clinical documentation Hospital MIS and analytics Patient experience Operational automation Predictive analytics Medical coding / classification Healthcare workflow automation The candidate should be able to work carefully with healthcare data and understand the importance of privacy, security, accuracy, explainability, and human oversight.

1. Speech & Multimodal AI

Experience in o…