We’re looking for an AI Lead who wants to take AI out of the lab and into production, in a space where accuracy really matters.You’ll set the technical direction for a team of AI/ML engineers and data scientists. That means owning the architecture and model strategy, deciding when to use prompting, RAG, fine-tuning, traditional ML or a hybrid, and building the frameworks for LLM evaluation, guardrails and hallucination control. You’ll also drive models from experimentation to reliable, scalable production services, with a sharp focus on latency, cost and monitoring. Just as important, you’ll mentor engineers and build reusable AI capabilities that can power multiple healthcare use cases.Exp: 6-9 years
Location: BangaloreKey ResponsibilitiesLead the technical design and development of AI/ML solutions for medical coding and RCM healthcare workflows.Define AI/ML architecture, model strategy, and technical standards across traditional ML, deep learning, NLP, Transformers, LLMs, RAG, and hybrid approaches.Design and implement scalable Generative AI architectures using LLMs, embeddings, vector databases, fine-tuning (LoRA/PEFT), RAG, and agentic AI patterns.Drive the full production lifecycle of AI models, including deployment, CI/CD, monitoring, drift detection, inference optimization, and cost efficiency.Establish best practices for model evaluation, hallucination mitigation, guardrails, grounding, and continuous improvement of accuracy, latency, and scalability.Mentor AI/ML engineers and data scientists, set engineering standards, and break down complex AI initiatives into clear, owned workstreams.Required QualificationsBachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.7+ years of experience in AI/ML, Data Science, or Machine Learning Engineering with strong hands-on Python expertise.Deep knowledge of Machine Learning, Deep Learning, NLP, Transformers, and modern AI architectures, with hands-on experience in PyTorch or TensorFlow.Proven hands-on experience with Generative AI, Large Language Models, RAG, embeddings, vector databases, prompt engineering, and LLM evaluation.Experience with LLM fine-tuning/customization (LoRA, PEFT, QLoRA) and designing/deploying AI/ML solutions in production environments.Strong understanding of MLOps, model lifecycle management, cloud platforms (AWS/Azure/GCP), and core software engineering practices (APIs, microservices, CI/CD, Git).