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.
What You Will DoCustomer Engagement Leadership
- 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 workplans, 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.
Agentic AI & LLM Engineering
- 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.
End-to-End AI Application Development
- 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.
Team Collaboration & Delivery Excellence
- Coordinate delivery across engineers, program managers, and solution architects - ensuring workstreams 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.
What We Are Looking ForExperience & Background
- 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, 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).
Skills & Capabilities
- Proven 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 - able to select the right tool for the problem, not just default to the latest LLM.
- Backend development skills: REST APIs, containerisation (Docker/Kubernetes), and CI/CD pipelines for AI applications.
- Strong executive communication - able to run a board-level briefing one hour and a technical design review the next, credibly.
- Comfortable with ambiguity and able to set direction for a team when requirements are incomplete or evolving.
How to Stand Out From the Crowd
- 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.
Why H2O.ai?
- Market leader in total rewards
- Remote-friendly culture
- Flexible working environment
- Be part of a world-class team
- Career growth
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