Senior AI Engineer - 2+ yrs exp [AI Healthcare startup]

Senior AI Engineer

2+ years in AI/ML/Data Science

Gurgaon, work from office

About Tap Health:

Tap Health helps people manage chronic conditions between doctor visits with AI-first, mobile-first digital therapeutics.

Our flagship, fully autonomous diabetes digital therapeutic makes daily disease management effortless, delivering real-time, human-level guidance tailored to each user’s habits, psychology, and health data.

By combining predictive AI, behavioral science and vernacular design, we are creating care products that are radically more scalable and easier to follow than legacy models.

We are reimagining chronic care to fit how people live

www.tap.health

Role Overview: Senior AI Engineer - 2+ yrs exp [AI Healthcare startup]

We are hiring a Lead AI Engineer in Gurgaon to drive AI-driven healthcare innovations.

The ideal candidate has 2+ years of AI/ML/Data Science experience with 1+ months of GenAI production experience, and 1+ year of hands-on GenAI product development. You need to have expertise in Agentic AI deployments, causal inference, and Bayesian modelling, with a strong foundation in LLMs and traditional models.

You will lead and collaborate with the AI, Engineering, and Product teams to build scalable, consumer-focused healthcare solutions. As an AI leader, you will be the go-to expert—the engineer others turn to when they hit roadblocks. You will mentor, collaborate and enable high product velocity while fostering a culture of continuous learning and innovation.

Skills & Experience

The ideal candidate should have the following qualities:

• Over 2 years of experience in AI/ML/Data Science
• Strong understanding of fine-tuning, optimization, and neural architectures.
• Hands-on experience with Python, PyTorch, and FastAI frameworks.
• Experience running production workloads on one or more hyperscalers (AWS, GCP, Azure, Oracle, DigitalOcean, etc.).
• In-depth knowledge of LLMs—how they work and their limitations.
• Ability to assess the advantages of fine-tuning, including dataset selection strategies.
• Understanding of Agentic AI frameworks, MCPs (Multi-Component Prompting), ACP (Adaptive Control Policies), and autonomous workflows.
• Familiarity with evaluation metrics for fine-tuned models and industry-specific public benchmarking standards in the healthcare domain.
• Knowledge of advanced statistical models, reinforcement learning, and Bayesian inference methods.
• Experience in Causal Inference and Experimental Science to improve product and marketing outcomes.
• Proficiency in querying and analyzing diverse datasets from multiple sources to build custom ML and optimization models.
• Comfortable with code reviews and standard coding practices using Python, Git, Cursor, and CodeRabbit.