Principal AI Engineer

h2o.ai is hiring a Principal AI Engineer for a hands-on, customer-facing role in the San Francisco Bay Area (hybrid). This position is built for engineers who enjoy owning outcomes end to end, partnering directly with enterprise teams, and helping complex organizations move from AI ideas to production-ready systems.

What you’ll get from h2o.ai

• Market leader in total rewards
• Remote-friendly culture
• Flexible working environment
• Be part of a world-class team
• Career growth

Responsibilities

• Lead end-to-end technical engagement with enterprise customers as the senior point of accountability for delivery quality, stakeholder relationships, and outcomes.
• Manage multiple concurrent engagement streams by coordinating workplans, resourcing, and milestones across cross-functional teams.
• Act as the primary technical escalation point for customer issues, proactively identifying risks and driving resolution across engineering, product, and leadership.
• Build trusted relationships with customer data science teams, engineering leads, and executive stakeholders, translating business needs into technical direction and back.
• Own pre-sales and proof-of-concept engagements, setting the technical strategy and ensuring demonstrations build genuine enterprise trust.
• Represent h2o.ai externally in customer workshops, executive briefings, and technical deep-dives as a credible senior voice.
• Design and build agentic AI systems and multi-agent frameworks to automate complex, multi-step enterprise workflows.
• Develop and deploy LLM-powered applications using RAG, fine-tuning, prompt engineering, function calling, and tool use.
• Implement guardrails, evaluation frameworks, and responsible AI controls to support production-grade reliability and safety.
• Stay current with the agentic AI landscape, including MCP, LLM orchestration frameworks, and reasoning models, and bring the best into customer engagements.
• Own the full development lifecycle across multiple streams, from problem framing and data exploration through model development, API integration, and production deployment.
• Build scalable backend services and APIs that expose AI capabilities to enterprise applications and workflows.
• Integrate AI models into customer environments (cloud, on-prem, and hybrid) while maintaining performance, stability, and maintainability at scale.
• Develop ML pipelines and LLMOps infrastructure for continuous model improvement and monitoring in production.
• Coordinate delivery across engineers, program managers, and solution architects to keep workstreams aligned, unblocked, and on plan.
• Set the technical bar by reviewing outputs, shaping architecture decisions, and ensuring engineering quality.
• Mentor and guide junior ML and solution engineers within engagements to build capability alongside delivery.
• Collaborate with h2o.ai product and engineering teams to surface customer feedback, shape roadmap input, and help resolve platform-level issues.

Requirements

• 8+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
• Proven experience leading technical delivery across complex, multi-stakeholder enterprise engagements.
• Demonstrable experience building LLM-powered applications (for example: RAG pipelines, agentic workflows, or fine-tuned models).
• Strong Python skills, plus ML framework experience (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent).
• Experience deploying AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes).
• Ability to manage multiple concurrent workstreams and coordinate cross-functional teams toward shared delivery milestones.
• Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
• Solid grounding in classical ML to select appropriate tools, not only the newest LLM approach.
• Backend development capability including REST APIs, containerisation (Docker/Kubernetes), and CI/CD pipelines for AI applications.
• Strong executive communication skills, including comfort running both executive briefings and technical design reviews.
• Comfort working with ambiguity and setting direction when requirements are incomplete or evolving.

Technologies

Python, PyTorch, TensorFlow, scikit-learn, LangChain, LlamaIndex, RAG, MCP, REST APIs, Docker, Kubernetes, CI/CD, AWS, Azure, GCP, RLHF

How to stand out

• Kaggle or competitive ML experience.
• Familiarity with h2o.ai products: Wave or H2O Document AI.
• Experience in financial services, healthcare, or other regulated industry AI deployments.
• Exposure to tabular foundation models, AutoML, or enterprise ML platforms.
• Prior experience in a customer-facing or field engineering role.

Location & compensation

San Francisco, CA (hybrid)

Salary: USD 175,000 - 200,000 per year