Senior AI Engineer

Join <H2O.ai> as a Senior AI Engineer, where you will design and ship end-to-end AI solutions for complex enterprise problems in the APAC region. This hands-on engineering role involves leading technical engagement with enterprise customers, managing multiple concurrent engagement streams, and building trusted relationships with customer data science teams and executive stakeholders. You will also design and build agentic AI systems, develop and deploy LLM-powered applications, and own the full development lifecycle across multiple streams.

• Concevoir et expédier des solutions d'IA de bout en bout pour des problèmes d'entreprise complexes.
• Diriger l'engagement technique de bout en bout avec les clients d'entreprise, en agissant en tant que point de responsabilité senior pour la qualité de la livraison.
• Gérer plusieurs flux d'engagements simultanément, en coordonnant les plans de travail, les ressources et les jalons.
• Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent)
• Experience deploying AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes)
• Demonstrable experience leading technical delivery across complex, multi-stakeholder enterprise engagements – not just executing within them
• 8+ 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
• Solid grounding in classical ML – able to select the right tool for the problem, not just default to the latest LLM
• Strong executive communication – able to run a board-level briefing one hour and a technical design review the next, credibly
• Comfortable with ambiguity and able to set direction for a team when requirements are incomplete or evolving
• Proven ability to manage multiple concurrent workstreams and coordinate cross-functional teams toward shared delivery milestones
• 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
• 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