●     Design, build, and optimize Retrieval-Augmented Generation (RAG) pipelines and agentic workflows to leverage LLMs on proprietary domain-specific knowledge bases.

●     Fine-tune open-source and proprietary foundation models (using techniques like LoRA, QLoRA) for specialized tasks such as medical clinical summarization, text extraction, and entity recognition.

●     Develop, train, and validate deep learning models (e.g., transformers, convolutional neural networks, and sequence models) to process unstructured text, medical imaging, or time-series physiological data.

●     Build and scale deep learning pipelines for feature extraction, semantic search, and multimodal applications.

●     Wrap machine learning and generative AI models into high-performance REST or gRPC APIs (using frameworks like FastAPI or Flask).

●     Deploy models efficiently using specialized inference engines (e.g., vLLM, Hugging Face TGI, Triton Inference Server) to minimize latency and optimize GPU memory utilization.

Requirements

•      Bachelor’s degree in Computer Science, Software Engineering, Statistics, or a highly quantitative discipline.

●     Master’s or PhD in Computer Science, Artificial Intelligence, Deep Learning, Data Science, or a quantitative field with a heavy focus on machine learning and neural networks.

●     1+ years of professional experience working strictly as an AI Engineer, NLP/ML Engineer, or Deep Learning Specialist.

●     Prior experience in healthcare, clinical informatics, hospital operations, or pharmaceutical environments is highly preferred but not mandatory.

Proven track record of building and deploying production-grade AI systems, LLM

Tools and Technologies :

●     Deep Learning & GenAI Orchestration: Python, Hugging Face (Transformers, PEFT), LangChain, LlamaIndex, LangGraph.

●     Vector Databases: Chroma, PGVector, Qdrant.

●     Model Deployment & Serving: vLLM, Triton, FastAPI, Docker, Git.

●     BI & Model Visualization (Preferred): Streamlit, Gradio, Plotly, Tableau.

●     Cloud & Infrastructure (Familiarity): Azure ML, AWS SageMaker.