Principal AI Engineer

Principal AI Engineer at h2o.ai is a hands-on, customer-facing role designing and shipping end-to-end AI solutions for complex enterprise needs. This is a hybrid position in the San Francisco Bay Area, where you will lead technical delivery across agentic AI systems, LLM applications, and production ML pipelines.

What you get

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

Responsibilities

You will own technical engagements from discovery through delivery, acting as the senior point of accountability for quality, stakeholder relationships, and outcomes.

• Lead end-to-end technical engagement with enterprise customers, coordinating delivery quality and measurable outcomes.
• Manage multiple concurrent engagement streams, aligning workplans, resourcing, and milestones across cross-functional teams.
• Serve as the primary 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.
• Lead pre-sales and proof-of-concept engagements by setting technical strategy and delivering demonstrations that earn enterprise trust.
• Represent h2o.ai externally in customer workshops, executive briefings, and technical deep-dives.
• 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 for production-grade reliability and safety.
• Stay current with the agentic AI landscape and bring relevant capabilities into customer engagements (including MCP, LLM orchestration frameworks, and reasoning models).
• 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) to support performance, stability, and maintainability at scale.
• Develop ML pipelines and LLMOps infrastructure for continuous model improvement and production monitoring.
• Coordinate delivery across engineers, program managers, and solution architects to keep workstreams aligned and unblocked.
• Set the technical bar through reviews, architecture guidance, and engineering quality across the team.
• Mentor and guide junior ML engineers and solution engineers to build capability during engagements.
• Collaborate with h2o.ai product and engineering teams to surface customer feedback, inform roadmap input, and resolve platform-level issues.

Requirements

• 8+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
• Proven ability to lead technical delivery across complex, multi-stakeholder enterprise engagements.
• Demonstrable experience building LLM-powered applications, including RAG pipelines, agentic workflows, fine-tuned models, or similar.
• Strong Python engineering skills; experience with PyTorch, TensorFlow, scikit-learn and LLM tooling such as LangChain or 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 delivery milestones.
• Deep understanding of modern GenAI concepts including prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
• Solid grounding in classical ML to select the right approach for the problem.
• Backend development experience including REST APIs, containerisation (Docker/Kubernetes), and CI/CD pipelines for AI applications.
• Strong executive communication, including running executive briefings and technical design reviews.
• Comfort working with ambiguity and setting direction when requirements evolve.

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, including 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 customer-facing or field engineering experience.

Location: San Francisco, CA (hybrid). Salary: USD 175,000 - 200,000 per year.