Lead,AI

Job Title: Lead, AI

Experience: 8-11 Years

Location: Hyderabad and Bengaluru

Company Overview:

Ensemble Health Partners India is at the forefront of innovation in the Revenue Cycle Management (RCM) space, leveraging modern technology to drive meaningful, real world impact. Our future ready platforms bring together AI driven analytics , intelligent data ingestion, workflow automation, and business intelligence built on a scalable, cloud native architecture. Our AI powered solutions are actively running in production, continuously optimizing processes and delivering data driven insights at scale. With the second largest market share in the U.S. RCM industry, a global workforce of 15,000+ professionals, and 12 technology patents , we deliver results through strong teams, proven processes, and flexible, modern technologies. As part of our continued growth, we have launched our Global Capability Center (GCC) in Hyderabad designed to serve as a strategic extension of our global operations. The GCC brings together technology, analytics, and RCM expertise to build scalable solutions, accelerate innovation, and support our long term vision of transforming healthcare operations. At Ensemble Health Partners India, we foster a culture of growth, collaboration, and innovation where your expertise is valued, your ideas are heard, and your work makes a measurable impact.

AI Engineer (Senior, Lead)

We are looking for an Applied AI Engineer to build, evaluate, and continuously improve clinical AI agents and supervised ML Models. You will work at the intersection of software engineering, LLM systems, evaluation, model improvement, and deep healthcare workflow understanding.

Your job is to turn frontier model capability into reliable production behavior: agents that read complex medical records, use the right clinical and coding context, call the right tools, produce auditable outputs, and improve real-world failures.

You will be embedded in hard healthcare problems, clinical documentation integrity, medical coding, denial prevention, appeals, revenue cycle workflows, and payer logic — and will own the loop from problem framing to agent design, evaluation, deployment, trace analysis, and ongoing improvement.

The ideal candidate is a strong engineer who thinks like an applied scientist: rigorous about measurement, comfortable with ambiguity, excited by messy real-world data, and motivated by closing the gap between impressive demos and dependable production systems.

Most AI roles are either too research-heavy or too product-light. This role sits in the middle. You will not only write prompts or run experiments. You will own whether an agent actually works in production. That means understanding the workflow, designing the system, building the evals, inspecting failures, improving the agent, and proving that the improvement matters.

Responsibilities:

• Design, build, and continuously improve production AI systems for complex healthcare workflows, including documentation, coding, denial management, appeals, and revenue cycle automation.
• Prototype new AI capabilities from 0 to 1, then harden them into reliable, explainable, auditable production systems with clear contracts, monitoring, evidence, and performance gates.
• Build rigorous evaluation and feedback loops using expert review, production logs, model outputs, and benchmarks to measure performance, regressions, reliability, cost, and business impact.
• Own outcomes end to end: understand the workflow, design the system, inspect failures, improve the system, and prove that the improvement matters.
• Set technical direction for a portfolio of projects, review designs, and mentor engineers as a technical lead.
• Partner with research, ML engineering, platform, and product teams on model selection, training, infrastructure, and Productionalization.

Educational Qualifications

• Bachelor’s degree in computer science, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience.
• Master’s or PhD in Computer Science, AI/ML, or a related field is a plus.

Industry Experience

• 5+ years of Applied Research, ML engineering, research engineering, software engineering, or applied AI experience.
• A track record of shipping and operating production systems, ideally in ambiguous, high-stakes domains.
• Prior experience in healthcare, revenue cycle management, or other regulated industries is a …