Lead AI Engineer

Responsibilities:

• Lead the design and development of LLM, GenAI, NLP, and deep learning solutions for healthcare use cases.
• Build and productionize AI models for processing clinical notes, EHR data, medical codes, and other structured/unstructured healthcare data.
• Design agentic AI and multi-agent workflows for complex healthcare revenue-cycle use cases.
• Develop robust NLP pipelines for clinical text understanding, information extraction, classification, summarisation, and reasoning.
• Fine-tune and evaluate LLMs using techniques such as prompt engineering, RAG, PEFT/LoRA, supervised fine-tuning, and model optimisation.
• Design scalable inference pipelines with a focus on latency, reliability, accuracy, and cost efficiency.
• Build evaluation frameworks to measure model performance, hallucination, factuality, explainability, and safety.
• Collaborate with engineering teams to deploy AI models into highly scalable production environments.
• Establish best practices around MLOps, model monitoring, experimentation, versioning, and continuous improvement.
• Work with large-scale healthcare datasets while maintaining data quality, privacy, security, and compliance requirements.
• Mentor AI/ML engineers and contribute to technical architecture and engineering standards.
• Stay current with advancements in LLMs, multimodal AI, agentic systems, retrieval systems, and healthcare AI and translate relevant research into production capabilities.
• Partner with Product and Clinical teams to convert business and healthcare requirements into scalable AI solutions.

Requirements:

• 7+ years of experience in AI/ML engineering, applied machine learning, NLP, or related fields.
• Strong hands-on programming experience in Python.
• Strong understanding of Machine Learning, Deep Learning, NLP, and Generative AI.
• Hands-on experience with LLMs such as GPT, Claude, Llama, Mistral, or similar models.
• Strong experience with RAG, embeddings, vector databases, prompt engineering, and LLM evaluation.
• Experience building and deploying ML/AI models in production.
• Strong experience with PyTorch and/or TensorFlow.
• Experience with ML/AI frameworks such as Hugging Face, LangChain, LlamaIndex, or equivalent.
• Strong understanding of model evaluation, experimentation, and performance optimisation.
• Experience working with AWS/GCP/Azure and cloud-based AI/ML infrastructure.
• Strong understanding of APIs, microservices, distributed systems, and production software engineering practices.
• Experience with Docker, Kubernetes, CI/CD, and MLOps.
• Strong problem-solving and analytical skills.