Lead AI Engineer

2 weeks ago

Gurgaon, Haryana, India Tapfinity AI Full time

Role - Lead AI Engineer

Experience: 3+ years in AI/ML, 6+ months of GenAI production experience, and 1+ year of hands-on GenAI product development.

Location: Gurgaon, work from office.

About Tap Health:

Tap Health is a deep-tech platform that transforms chronic care management with AI and makes healthcare accessible to millions. Powered by our proprietary Medical AI model, VaiD, Tap Health is designed to redefine chronic illness management through AI-driven, hyper-personalized care.

We are developing next-generation Digital Therapeutic products that deliver daily structured digital therapeutics, lifestyle interventions, personalized nutrition and exercise guidance, and real-time monitoring, all without human intervention. Our platform helps users manage conditions such as diabetes, hypertension, PCOS, pregnancy, and asthma.

Leveraging AI and adaptive algorithms, Tap Health dynamically personalizes each user's care journey, delivering enhanced insights, interventions, motivation, adherence, and clinical outcomes at a fraction of traditional healthcare costs.

Beyond Digital Therapeutics, the Tap Health platform provides free primary illness diagnosis based on symptoms and instant health advice through a seamless, voice-first experience.

Role Overview: Lead AI Engineer

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

The ideal candidate has expertise in GenAI, Agentic AI deployments, causal inference, Bayesian modeling, LLMs, and traditional models. You will collaborate with the AI, Engineering, and Product teams to build scalable, consumer-focused healthcare solutions.

Skills & Experience:

  • Fine-tuning and optimization: Strong understanding of fine-tuning, optimization, and neural architectures.
  • Programming languages and frameworks: Hands-on experience with Python, PyTorch, FastAI, and Git.
  • Hyperscalers: Experience running production workloads on one or more hyperscalers (AWS, GCP, Azure, Oracle, DigitalOcean, etc.).
  • LLMs: In-depth knowledge of LLMs—how they work and their limitations.
  • Agentic AI: Understanding of Agentic AI frameworks, MCPs (Multi-Component Prompting), ACP (Adaptive Control Policies), and autonomous workflows.
  • Evaluation metrics: Familiarity with evaluation metrics for fine-tuned models and industry-specific public benchmarking standards in the healthcare domain.
  • Causal Inference and Experimentation Science: Knowledge of advanced statistical models, reinforcement learning, and Bayesian inference methods.
  • Dataset analysis: Proficiency in querying and analyzing diverse datasets from multiple sources to build custom ML and optimization models.

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