AI Engineer

• 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.

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.