Principal AI Engineer

• We are looking for a Principal 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
• Lead end-to-end technical engagement with enterprise customers, acting as the senior point of accountability for delivery quality, stakeholder relationships, and outcomes
• Manage multiple concurrent engagement streams simultaneously – coordinating workplans, resourcing, and milestones across cross-functional teams
• Serve as the primary technical escalation point for customer issues, proactively identifying risks and driving resolution across engineering, product, and leadership
• Build and maintain trusted relationships with customer data science teams, engineering leads, and executive stakeholders – translating business needs into technical direction and back again
• Lead pre-sales and proof-of-concept engagements, setting the technical strategy and ensuring the team delivers demonstrations that build genuine enterprise trust
• Represent H2O.ai externally at customer workshops, executive briefings, and technical deep-dives as a credible senior voice
• Design and build agentic AI systems and multi-agent frameworks that automate complex, multi-step enterprise workflows
• Develop and deploy LLM-powered applications using 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 into customer engagements
• Own the full development lifecycle across multiple streams: 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
• Coordinate delivery across engineers, program managers, and solution architects – ensuring workstreams are aligned, unblocked, and progressing to plan
• Set the technical bar for the engagements you lead, reviewing outputs, shaping architecture decisions, and ensuring engineering quality across the team
• Mentor and guide junior ML engineers and solution engineers within engagements, building team capability alongside delivery
• Collaborate closely with H2O.ai product and engineering teams to surface customer feedback, shape roadmap input, and resolve platform-level issues

Benefits

• Remote-friendly culture
• Flexible working environment
• Career growth- Backend development skills: REST APIs, containerisation (Docker/Kubernetes), and CI/CD pipelines for AI applications
• Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps
• Demonstrable experience leading technical delivery across complex, multi-stakeholder enterprise engagements – not just executing within them
• Experience deploying AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes)
• Demonstrable experience building LLM-powered applications – RAG pipelines, agentic workflows, fine-tuned models, or similar
• Strong executive communication – able to run a board-level briefing one hour and a technical design review the next, credibly
• Proven ability to manage multiple concurrent workstreams and coordinate cross-functional teams toward shared delivery milestones
• Solid grounding in classical ML – able to select the right tool for the problem, not just default to the latest LLM
• Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent)
• Comfortable with ambiguity and able to set direction for a team when requirements are incomplete or evolving
• 8+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment
• Kaggle or competitive ML experience
• Familiarity with H2O.ai products, Wave, or H2O Document AI
• Experience in financial services, healthcare, or other regulated industry AI deployments
• Exposure to tabular foundation models, AutoML, or enterprise ML platforms
• Prior experience in a customer-facing or field engineering role