Jobs at H2O.ai

Lead customer-facing, end-to-end AI engagements: design and ship agentic AI systems, LLM applications, and production ML pipelines; build scalable backend services and integrations; implement guardrails and monitoring; coordinate cross-functional delivery; mentor junior engineers; and represent H2O.ai in technical briefings and pre-sales activities.

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.

Lead end-to-end technical engagements to design, build, and deploy agentic AI and LLM-powered applications (RAG, fine-tuning, multi-agent systems). Own full ML lifecycle, backend services, integrations across cloud/on‑prem, and production LLMOps. Coordinate cross-functional teams, mentor junior engineers, and represent H2O.ai in customer workshops and executive briefings to deliver production-grade, compliant AI solutions.

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.

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.

Design, build, and deploy end-to-end agentic AI and LLM applications for enterprise customers. Responsibilities include model development (RAG, fine-tuning), production ML pipelines, Kubernetes/Helm deployments (cloud, on-prem, air-gapped), LLMOps and monitoring, customer-facing technical delivery, troubleshooting, workshops, and ensuring secure, production-grade AI with guardrails and performance sizing.

Design, build, and maintain scalable back-end services (Python/Java/Go) and responsive front-end interfaces (React/TypeScript). Translate product concepts into technical designs, implement CI/CD pipelines, perform code reviews and testing, monitor and optimize production systems, and collaborate with cross-functional teams to deliver reliable cloud-native, microservices-based software.

The Senior Sales Engineer will leverage technical expertise in ML and Generative AI to support enterprise customers with solution design and scaling AI applications. Responsibilities include partnering with sales for pre-sales activities, executing proof of values, and guiding customers through MLOps and deployments.

As an AI Engineer, you will design and deploy AI solutions, including LLM applications, integrating them into enterprise environments, and collaborating with customers to resolve complex challenges.

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.

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