Jobs at H2O.ai

Supports customers by troubleshooting technical issues, reproducing bugs, identifying root causes, and delivering fixes. Designs features, writes production code and unit tests, reviews code, and contributes to automated testing. Develops infrastructure with Terraform and Helm while working extensively with Kubernetes, Docker, cloud platforms, Linux, microservices, and MLOps systems for deploying and monitoring machine learning models.

Troubleshoot customer issues, analyze and reproduce bugs, identify root causes, implement fixes, design features, write code and unit tests, and contribute to automated testing. Review developer designs and code, improve engineering practices, and manage infrastructure changes using Terraform and Helm. The role requires strong software engineering, Kubernetes, Docker, Linux, cloud, networking, and customer communication skills.

Support customers by troubleshooting technical issues, analyzing and reproducing bugs, identifying root causes, and delivering fixes. Design features, write production code and unit tests, contribute to automated testing, review code, and improve engineering practices. Develop infrastructure using Terraform and Helm, while working extensively with Kubernetes, Docker, Linux, cloud platforms, microservices, databases, and networking technologies. The role requires strong communication and customer-facing skills.

Leads customer-facing AI and machine learning engineering activities, including solution architecture, demonstrations, proof-of-concept delivery, and deployment of end-to-end ML workflows. Serves as a trusted technical advisor during sales engagements, translates business needs into scalable cloud-native solutions, manages customer technical relationships, and supports successful project delivery. Manages, mentors, and develops a team of AI and machine learning engineers.

Leads pre-sales technical discovery, solution architecture, demonstrations, proof-of-value projects, and enterprise AI deployments. Designs predictive, generative, and agentic AI solutions across cloud, hybrid, and on-premises environments. Advises customers on LLM fine-tuning, RAG, agentic workflows, MLOps, Kubernetes, governance, monitoring, and integrations. Partners with Sales and Product, develops reusable solution assets, influences the roadmap, and represents H2O.ai at industry events. Travel up to 50% is required.

As a Senior Sales Engineer at H2O.ai, you'll lead pre-sales efforts by leveraging technical expertise in AI to design solutions for enterprise customers, drive ROI demonstrations, and guide AI adoption. You'll collaborate with multiple teams and influence product roadmaps while showcasing hands-on skill with LLMs and cloud-based deployments.

Support accounts payable, travel and expense processing, cash management, bank reconciliations, prepaids, accruals, journal entries, and month-end close. Maintain vendor records, assist with 1099 reporting, audits, compliance, cash forecasting, internal controls, and accounting automation. Partner cross-functionally to ensure accurate expense classification and invoice processing while improving ERP and finance operations.

Supports accounts payable, travel and expense processing, cash management, bank reconciliations, month-end close, journal entries, vendor records, 1099 reporting, and audit activities. The role partners cross-functionally to ensure accurate expense classification, contributes to cash forecasting and internal controls, and helps automate and improve accounting systems. This is a temporary position with potential full-time conversion.

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

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.

Design, build, and deploy end-to-end agentic AI and LLM applications and predictive ML models for enterprise customers. Own full lifecycle from data exploration and model development to APIs, MLOps/LLMOps pipelines, and production deployment across cloud and on-prem. Implement guardrails, evaluation frameworks, and monitoring. Work directly with customers, contribute to pre-sales proofs-of-concept, and collaborate with cross-functional teams to deliver production-grade, secure, and maintainable AI solutions.

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.

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.

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.