Jobs at H2O.ai

Designs and delivers production-grade AI solutions for enterprise customers, including agentic AI systems, LLM applications, RAG pipelines, ML services, and LLMOps infrastructure. Leads customer engagements, pre-sales proofs of concept, technical strategy, architecture, delivery coordination, stakeholder communication, and issue resolution. Builds backend APIs, deploys solutions across cloud, on-premises, and hybrid environments, establishes responsible AI controls, and mentors engineering teams.

Own pipeline growth through account-based marketing and demand generation for strategic enterprise accounts. Build and execute 1:1, 1:few, and 1:many ABM programs; manage intent data, enrichment, scoring, routing, campaigns, nurture, email, webinars, paid media, and events. Partner closely with sales and RevOps, manage HubSpot and Salesforce funnel operations, own the demand-generation budget and metrics, and build the team over time.

Own pipeline generation and account-based marketing for strategic enterprise accounts. Build and execute 1:1, 1:few, and 1:many ABM programs; manage intent signals, enrichment, scoring, routing, campaigns, nurture, email, webinars, paid media, and events. Partner closely with sales and RevOps, manage HubSpot and Salesforce funnel operations, own demand-generation metrics and budget, and build the team over time.

Deploys and optimizes secure, scalable AWS and Kubernetes environments for enterprise customers. Builds infrastructure automation with Terraform, Ansible, and CloudFormation; maintains CI/CD pipelines, observability, security controls, and production Kubernetes systems. Troubleshoots Linux, container, networking, and distributed-system issues, supports customer deployments and incidents, translates technical requirements into implementation plans, and collaborates with customer-facing and internal engineering teams.

Lead technical pre-sales for enterprise AI and machine learning solutions. Responsibilities include customer discovery, solution architecture, AI/ML and GenAI demonstrations, proof-of-value projects, agentic workflow design, cloud and on-premises deployment planning, MLOps guidance, executive presentations, and customer-driven product feedback. The role supports strategic accounts across industries and requires travel up to 50%.

Designs, deploys, and operates secure, scalable AWS and Kubernetes environments for enterprise customers. Builds infrastructure-as-code automation, CI/CD pipelines, observability, and production release processes. Troubleshoots Linux, containers, networking, and distributed systems while improving reliability, security, and performance. Partners directly with customers and internal teams on technical requirements, deployment architecture, incident resolution, and continuous improvement.

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.

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.

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.

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.

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.