AI Engineering & Data Science Lead

About Tapza

At Tapza, we're building an AI-native operating system for healthcare providers, including pharmacies, clinics, and hospitals.

Our philosophy is simple:

The best healthcare software might be the software healthcare professionals barely have to operate.

The Opportunity

We're looking for an AI Engineering & Data Science Lead to own the technical direction, architecture, and execution of AI across Tapza's healthcare products.

This is a hands-on technical leadership role.

You'll work directly with the founding team, make consequential engineering decisions, build production-grade AI systems, and gradually develop the AI engineering team around you.

We're looking for someone who has already taken AI systems beyond prototypes and successfully deployed them in real-world environments.

What You'll Own

Agentic AI & LLM Engineering

Architect, build, and deploy production-grade AI agents, RAG systems, and intelligent automation workflows.

Develop reliable agent orchestration, tool integrations, structured data extraction, and enterprise system integrations.

Implement robust guardrails, human-in-the-loop controls, data security, observability, and failure recovery.

Own model selection, evaluation, and optimization for accuracy, latency, reliability, scalability, and inference cost.

Production AI Engineering

Design scalable AI services, inference pipelines, and integrations with backend systems.

Implement observability, automated testing, model monitoring, versioning, and rollback mechanisms.

Optimize inference performance, reliability, and infrastructure costs.

Establish secure data handling, access controls, and privacy-aware AI architecture.

What We're Looking For

Required

5+ years of relevant software engineering, machine learning, or applied AI experience.

Proven experience in architecting, building, and deploying AI/ML systems used in production.

Strong Python programming and software engineering fundamentals.

Experience with machine learning fundamentals, statistical evaluation, and model performance measurement.

Experience building APIs, data pipelines, and cloud-deployed services.

Practical understanding of model evaluation, latency optimization, inference economics, security, and production reliability.

Ability to independently own complex technical problems from architecture through deployment.

Experience mentoring engineers, reviewing technical designs, or leading technical delivery.

Good to Have

Experience with speech AI, multilingual NLP, OCR, or document intelligence.

Experience with time-series forecasting and predictive analytics.

Familiarity with AWS and production MLOps infrastructure.

Experience with healthcare, healthtech, or other regulated industries.

Experience building systems that process sensitive information.

Familiarity with clinical workflows, interoperability standards, or ABDM.

Healthcare domain experience is an advantage, not a requirement. Strong production engineering experience matters more.

Why Join Tapza?

Technical ownership: Shape the architecture and engineering direction of AI across multiple healthcare products.

Real-world complexity: Solve problems involving reliability, multilingual environments, latency, inference costs, security, and imperfect data.

Leadership opportunity: Help establish and grow Tapza's AI engineering function.

Long-term upside: Equity participation and the potential to progress into a broader AI leadership role as the company grows.

Compensation

Competitive compensation within our current startup-stage budget, along with meaningful equity participation.

Detailed compensation and equity terms will be discussed during the hiring process.

How to Apply

We're especially interested in understanding what you've actually built and shipped.

Along with your resume or LinkedIn profile,

please share:

One AI/ML system you personally architected or substantially built and deployed in production.

Your specific technical contribution, the hardest engineering challenge, and how you solved it.

The scale of the system and how you measured its reliability, accuracy, latency, or cost.

GitHub repositories, technical write-ups, architecture diagrams, or project demonstrations are welcome where publicly shareable.

We're looking for demonstrated engineering ownership—not just familiarity with AI tools and frameworks.

Questions::

How many years of professional experience do you have building and deploying AI/ML systems?

Have you personally architected and deployed an AI/ML system used by real customers in production?

Have you worked hands-on with production LLM agents, RAG systems, or comparable complex AI pipelines?

Are you willing to work full-time on-site in Hyderabad?

Briefly describe one production AI system you owned, your contribution, and how you evaluated its performance.