Machine Learning Engineer

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: Bangalore

Key Responsibilities

• Lead 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 Qualifications

• Bachelor’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).