Principal AI Engineer

About This Opportunity

We are looking for a Principal AI Engineer who builds things that matter. You will design and ship end‑to‑end AI solutions for some of APAC’s most complex enterprise problems – spanning agentic AI systems, LLM applications, and production ML pipelines. This is a hands‑on engineering role embedded within a customer‑facing field team, meaning your work will be seen, used, and evaluated by real enterprises from day one.

You will work alongside Kaggle Grandmasters, ML engineers, and domain experts to deliver AI that goes beyond demos – into production, into workflows, and into measurable business outcomes.

This position is based in Dallas, Texas and requires onsite customer interfacing.

• Lead end‑to‑end technical engagement with enterprise customers, acting as the senior point of accountability for delivery quality, stakeholder relationships, and outcomes.
• Manage multiple concurrent engagement streams simultaneously – coordinating work plans, resourcing, and milestones across cross‑functional teams.
• Serve as the primary technical escalation point for customer issues, proactively identifying risks and driving resolution across engineering, product, and leadership.
• Build and maintain trusted relationships with customer data science teams, engineering leads, and executive stakeholders – translating business needs into technical direction and back again.
• Lead pre‑sales and proof‑of‑concept engagements, setting the technical strategy and ensuring the team delivers demonstrations that build genuine enterprise trust.
• Represent H2O.ai externally at customer workshops, executive briefings, and technical deep‑dives as a credible senior voice.

• Design and build agentic AI systems and multi‑agent frameworks that 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 ensure production‑grade reliability and safety.
• Stay current with the rapidly evolving agentic AI landscape – MCP, LLM orchestration frameworks, 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 – ensuring performance, stability, and maintainability at scale.
• Develop ML pipelines and LLMOps infrastructure that support continuous model improvement and monitoring in production.

• Coordinate delivery across engineers, program managers, and solution architects – ensuring work streams are aligned, unblocked, and progressing to plan.
• Set the technical bar for the engagements you lead, reviewing outputs, shaping architecture decisions, and ensuring engineering quality across the team.
• Mentor and guide junior ML engineers and solution engineers within engagements, building team capability alongside delivery.
• Collaborate closely with H2O.ai product and engineering teams to surface customer feedback, shape roadmap input, and resolve platform‑level issues.

• 8+ years of hands‑on AI/ML engineering experience, including end‑to‑end model development and production deployment.
• Demonstrable experience leading technical delivery across complex, multi‑stakeholder enterprise engagements – not just executing within them.
• Demonstrable experience building LLM‑powered applications – RAG pipelines, agentic workflows, fine‑tuned models, or similar.
• Strong Python engineering skills; experience with ML frameworks (PyTorch, Tensor Flow, scikit‑learn) and LLM tooling (Lang Chain, Llama Index, or equivalent).
• Experience deploying AI services in cloud or enterprise environments (AWS, Azure, GCP, on‑prem Kubernetes).