Technical Program Manager, H2O.ai

• 3+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment
• Demonstrable experience building LLM-powered applications - RAG pipelines, agentic workflows, fine-tuned models, or similar
• Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent)
• Experience deploying models and AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes)
• Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps
• Solid grounding in classical ML - able to select the right tool for the problem, not just default to the latest LLM
• Backend development skills: REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications
• Strong problem-solving instincts - comfortable with ambiguity, able to move fast without sacrificing engineering quality
• Clear communicator who can explain complex AI systems to non-technical stakeholders without oversimplifying

Desirable

• We are looking for a Senior AI Engineer who builds things that matter
• You will design and ship end-to-end AI solutions for some of APAC's most complex enterprise problems - spanning agentic AI systems, LLM applications, and production ML pipelines
• This is a hands-on engineering role embedded within a customer-facing field team, meaning your work will be seen, used, and evaluated by real enterprises from day one
• You will work alongside Kaggle Grandmasters, ML engineers, and domain experts to deliver AI that goes beyond demos - into production, into workflows, and into measurable business outcomes
• Agentic AI & LLM Engineering:
• Design and build agentic AI systems and multi-agent frameworks that automate complex, multi-step workflows for enterprise customers
• Develop and deploy LLM-powered applications using techniques including RAG, fine-tuning, prompt engineering, function calling, and tool use
• Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production-grade reliability and safety
• Stay current with the rapidly evolving agentic AI landscape - MCP, LLM orchestration frameworks, reasoning models - and bring the best of it into customer engagements
• End-to-End AI Application Development:
• Own the full development lifecycle: from problem framing and data exploration through model development, API integration, and production deployment
• Build scalable backend services and APIs that expose AI capabilities to enterprise applications and workflows
• Integrate AI models into customer environments - cloud, on-prem, and hybrid - ensuring performance, stability, and maintainability at scale
• Develop ML pipelines and LLMOps infrastructure that support continuous model improvement and monitoring in production
• Customer Engagement & Delivery:
• Work directly with customer data scientists, engineers, and business stakeholders to translate real-world problems into AI solutions
• Contribute to pre-sales and proof-of-concept engagements - building fast, credible demonstrations that win technical trust
• Communicate clearly across audiences: from detailed technical design reviews with engineering teams to outcome-focused updates for business stakeholders
• Collaborate closely with Program Managers, Solution Engineers, and Kaggle Grandmasters to deliver cohesive, high-quality solutions