Job Title

Role -Senior AI Engineer Experience:3+ years in AI/ML Location:Gurgaon, work from officeAbout Tap Health:Tap Health is a deep-tech startup transforming chronic care with AI and changing how people access health information. We build next-generation, AI-driven digital therapeutics for diabetes, PCOS, hypertension, asthma, pregnancy, obesity and more, eliminating the need for human support while significantly reducing costs, improving engagement and boosting outcomes.Tap Health's fully autonomous digital therapeutic for diabetes simplifies management by delivering real-time, daily guidance to optimize health outcomes at less than 10% of the cost of legacy products. Powered by adaptive AI and clinical protocols, it dynamically personalizes each user’s care journey, delivering tailored insights, lifestyle interventions, motivational nudges, adherence support, and improved clinical outcomes.Beyond digital therapeutics, Tap Health’s Health Assistant assists users in primary symptom diagnosis based on their inputs and provides instant health advice through a seamless, voice-first experience.Overview: Lead AI EngineerWe are hiring a full-time Lead AI Engineer in Gurgaon to drive AI-driven healthcare innovations.The ideal candidate has 3+ years of AI/ML experience, 6+ months of GenAI production experience, and 1+ year of hands-on GenAI product development. You need to have a strong data science background and have expertise in GenAI, Agentic AI deployments, causal inference, and Bayesian modeling, with a strong foundation in LLMs and traditional models.You will be collaborating with the AI, Engineering, and Product teams to build scalable, consumer-focused healthcare solutions. As a Lead AI Engineer, 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 & ExperienceThe ideal candidate should have the following qualities: 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 Experimentation 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.