Jobs at H2O.ai

Lead vulnerability management for a containerized cloud platform: triage scan results, remediate vulnerabilities hands-on (upgrade deps, rebuild images, patch code), coordinate fixes with engineering, support FedRAMP/SOC2 compliance, extend automation and tooling, engage customer security teams, and participate in incident response and security documentation.

Design, build, and deploy a Kubernetes-based cloud platform managing AI engine lifecycles. Develop operators, APIs (gRPC/REST), microservices, and CI/CD deployments (Helm/GitOps), ensure container hardening and production reliability, and mentor teammates through design-to-delivery ownership.

Own and grow a portfolio of large enterprise and Global 2000 accounts through multi-year strategic plans. Build C-level relationships, generate net-new and expansion pipeline, lead complex sales cycles, coordinate cross-functional teams, quantify ROI with value-based selling, execute partner co-sell strategies, maintain forecasting and CRM hygiene, and travel as needed for strategic engagements.

Sell H2O.ai's enterprise AI platform to large accounts and Global 2000 customers. Build C-level relationships, develop multi-year account plans, generate net-new and expansion pipeline, lead complex multi-stakeholder sales cycles, coordinate cross-functional teams, quantify ROI with value-based selling, and leverage partner ecosystems to close strategic deals while maintaining forecasting and CRM discipline.

The AI Engineer will develop and deploy AI applications, collaborate on the end-to-end pipeline, create backend services, and develop frontend interfaces.

Design, deploy, and maintain secure, scalable AWS and Kubernetes-based cloud environments using IaC and CI/CD. Automate provisioning, implement observability, troubleshoot containerized workloads, and work directly with enterprise customers to deliver production-grade solutions and improve operational practices.

Design and deliver end-to-end enterprise AI solutions (predictive ML, GenAI, agentic systems). Lead delivery execution, translate business problems to technical designs, implement production pipelines, ensure ML lifecycle best practices, and act as technical customer lead.

Develop, deploy, and maintain scalable microservices and data-heavy applications using Kubernetes, Go, Python, Protobuf, gRPC, REST APIs, and streaming technologies. Optimize large datasets, work with Kafka and Helm, and collaborate with senior engineers to deliver reliable enterprise solutions.

Customer-facing role delivering end-to-end Generative/Agentic AI solutions: problem definition, data prep, model development, deployment, monitoring, LLM selection/fine-tuning/RAG, training workshops, and solution design using H2O tools to translate technical outputs into business value.

Lead development of automation frameworks and tools to automate system, acceptance, and integration tests. Design, build, deploy scalable microservices using Go/Java/Python, Kubernetes, Protobuf/gRPC and REST. Optimize large datasets, work with streaming data (Kafka), deploy with Helm, debug and maintain high-quality code, and mentor engineers.

Design, deploy, and maintain secure, scalable cloud environments and production-grade Kubernetes clusters on AWS. Automate infrastructure with IaC (Terraform/Ansible/CloudFormation), build CI/CD pipelines, implement observability, troubleshoot containers and distributed systems, and work directly with enterprise customers to support deployments and incident resolution.

Support Singapore marketing team with event coordination, administrative tasks, social media and content scheduling, marketing data tracking, collateral creation, and partner portal/build tasks while gaining exposure to B2B tech marketing and generative AI.

The Senior AI Engineer will design and implement AI solutions, focusing on agentic AI and LLM applications, while engaging with customers to produce measurable business outcomes.

Lead pre-sales technical engagements for enterprise AI adoption: run discovery, design architectures and agentic/GenAI solutions, execute demos/POCs, guide cloud/Kubernetes deployments, and influence product direction while supporting sales and customers through pilots to production.

The Technical Program Manager will oversee AI program delivery, engage with C-suite stakeholders, drive adoption, and enhance customer success through effective management of enterprise accounts and relationships.

Partner with stakeholders to design, build, validate, and deploy predictive and generative AI/ML solutions. Perform end-to-end data science work (exploration, feature engineering, modelling, evaluation), translate results into business insights, explore GenAI approaches (LLMs, RAG, agentic workflows), and collaborate with engineering on productionisation and MLOps.

Lead technical recruiting in Australia, partner with senior leaders to design hiring strategies, build scalable sourcing pipelines, screen and present candidates, manage interview and offer processes, and deliver data-driven market insights while ensuring a high-quality candidate experience and compliance.

Design, build, and deploy production-grade AI applications and agentic systems. Own end-to-end AI solutions including data preparation, feature engineering, model development, deployment, monitoring, and governance. Architect GenAI systems (RAG, LLMs/SLMs, agents), build backend services/APIs, guide customers on adoption and responsible AI, and deliver technical enablement.

Design, build, and deploy end-to-end AI solutions including predictive ML, GenAI, RAG, and agentic systems. Own data preparation, feature engineering, model development, deployment, monitoring, and customer enablement. Build scalable backend services and APIs, implement production-grade GenAI pipelines, and guide customers on governance, evaluation, and adoption while collaborating with product and engineering teams.

Design, build, and deploy production AI applications and agent-based systems using the H2O AI platform. Contribute to end-to-end ML workflows (data prep, feature engineering, training, evaluation, deployment, monitoring), develop backend services and APIs, support customer implementations, run technical workshops, and collaborate with cross-functional teams to deliver measurable enterprise AI solutions.