Senior AI Engineer

Founded in 2012, H2O.ai is on a mission to democratize AI. As the world’s leading agentic AI company, H2O.ai converges Generative and Predictive AI to help enterprises and public sector agencies develop purpose-built GenAI applications on their private data. With a focus on Sovereign AI—secure, compliant, and infrastructure-flexible deployments—H2O.ai delivers solutions that align with the highest standards of data privacy and control. Our open-source technology is trusted by over 20,000 organizations worldwide, including more than half of the Fortune 500. H2O.ai powers AI transformation for companies like AT&T, Commonwealth Bank of Australia, Chipotle, Workday, Progressive Insurance, and NIH. H2O.ai partners include NVIDIA, Dell Technologies, Deloitte, Ernst & Young (EY), Snowflake, AWS, Google Cloud Platform (GCP), VAST Data and MinIO. H2O.ai’s AI for Good program supports nonprofit groups, foundations, and communities in advancing education, healthcare, and environmental conservation. With a vibrant community of 2 million data scientists worldwide, H2O.ai aims to co-create valuable AI applications for all users. H2O.ai has raised 256 million from investors, including Commonwealth Bank, NVIDIA, Goldman Sachs, Wells Fargo, Capital One, Nexus Ventures and New York Life. For more information, visit www.h2o.ai . About This Opportunity 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. This position is based in Singapore. What You Will Do AI, Agentic AI & LLM Engineering Design and build agentic AI systems and multi-agent frameworks that automate complex, multi-step workflows for enterprise customers. Develop LLM-powered applications using RAG, fine-tuning, prompt engineering, function calling and tool use. Build and deploy classical ML and deep learning solutions (e.g. forecasting, classification, tabular models) where they outperform or complement LLM-based approaches. Implement guardrails, human-in-the-loop checkpoints, and evaluation frameworks to ensure responsible, production-grade AI behavior. End-to-End AI Application Development Own the full development lifecycle from problem framing, data exploration, cleaning through integration, testing and production deployment. Build scalable backend services and APIs that expose AI capabilities to enterprise applications. Deploy and operate models and services in customer environments, including cloud, on-prem, and fully air-gapped, covering infrastructure provisioning, Kubernetes, Helm, ingress/TLS and GPU enablement. Right-size deployments: load-test serving stacks to measure real throughput, latency and concurrency, then turn those results into concrete GPU sizing guidance customers can plan and budget against. Build LLMOps infrastructure for continuous monitoring and improvement in production. Customer Engagement & Delivery Serve as the customer-facing point of contact across a range of stakeholder profiles, including data scientists, engineers and business leaders, translating real-world problems into AI solutions. Triage and resolve technical customer support tickets from initial diagnosis through to resolution. Run hands-on training, workshops and enablement sessions to help customer teams get the most out of the platform. Collaborate closely with internal stakeholders, including Product Managers, Engineering, and Kaggle Grandmasters to deliver cohesive, high-quality solutions. Contribute to pre-sales and proof-of-concept engagements: building fast, credible demonstrations that win technical trust, and scoping engagements realistically. Solid project management skills: able to plan, prioritize and track multiple concurrent customer engagements to completion. End-to-end ownership of what is shipped into a customer environment. What We Are Looking For Experience & Background 5+ years of hands-on engineering experience, including 2+ years working directly on Agentic AI systems with a track record of end-to-end AI application development and deploying applications into production. Demonstrable experience building LLM-powered applications: RAG pipelines, agentic workflows, fine-tuned models or similar. Experience deploying models and AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes). Experience working in security-accredited or fully air-gapped environments with no internet egress: offline delivery, image mirroring, private registries and dependency bundling. Skills & Capabilities Deep understanding of modern GenAI and Agentic AI concepts: prompt engineering RAG, fine-tuning, model evaluation, guardrails, LLMOps and MCP. Good understanding of traditional machine learning deep learning, feature engineering and evaluation metrics. Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent). Able to serve open-weight models with vLLM (or equivalent) across single and multi-node setups, sizing deployments from a model config and tuning key flags (tensor parallelism, max model length, GPU memory utilization, concurrency). Fluent in serving-performance trade-offs: time-to-first-token, inter-token latency, throughput, concurrency and context length. Working knowledge of AWS fundamentals (EC2, GPU instances, VPC, IAM, S3, CloudWatch) and GPU cost optimization. Kubernetes and Helm fluency: deploying, upgrading, and debugging workloads. Backend development skills: REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications. Experience with observability for LLM systems: log aggregation and monitoring (e.g. Fluentd, Prometheus, Grafana) and LLM-specific tracing tooling (e.g. Langfuse or equivalent). Strong problem-solving instincts: comfortable with ambiguity, able to move fast without sacrificing engineering quality. Comfortable operating within change-control or accreditation processes where fixes cannot be pushed freely. Clear communicator who can explain complex AI systems and technical constraints to non-technical stakeholders. Able to write good, consistent technical documentation, both internal (runbooks, sizing notes, post-incident write-ups) and customer-facing. How to Stand Out From the Crowd Experience in public sector, financial services, healthcare or other regulated industry AI deployments. Prior experience in a customer-facing or forward deployed engineering role. Keeps up with the latest advancements in the AI and agentic AI space and is able to incorporate them into client deliverables. Working knowledge of hardening practices relevant to enterprise/public-sector deployments: mTLS, log masking and redaction, secrets management, RBAC and audit requirements. Kaggle or competitive ML experience. Why H2O.ai? Market leader in total rewards Remote-friendly culture Flexible working environment Be part of a world-class team Career growth H2O.ai is committed to creating a diverse and inclusive culture. All qualified applicants will receive consideration for employment without regard to their race, ethnicity, religion, gender, sexual orientation, age, disability status or any other legally protected basis. H2O.ai is an innovative AI cloud platform company, leading the mission to democratize AI for everyone. Thousands of organizations from all over the world have used our cutting-edge technology across a variety of industries. We’ve made it easy for people at all levels to generate breakthrough solutions to complex business problems and advance the discovery of new ideas and revenue streams. We push the boundaries of what is possible with artificial intelligence. H2O.ai employs the world’s top Kaggle Grandmasters, the community of best-in-the-world machine learning practitioners and data scientists. A strong AI for Good ethos and responsible AI drive the company’s purpose. Please visit www.H2O.ai to learn more. #LI-Hybrid For information about how H2O.ai collects, uses, and protects your personal information during the recruitment process, please review our Candidate Privacy Notice.