Lead Forward Deployed Engineer, AI
H2O.ai · United States
- Hybrid
- Full-time
- $150,000 / year
- United States
Job highlights
- Lead AI solutions for enterprise customers.
- Build agentic AI and LLM applications.
- Develop end-to-end AI pipelines.
- Collaborate with top AI talent.
- Drive measurable business outcomes.
About the role
Lead Forward Deployed Engineer AI - H2O.ai
H2O.ai is on a mission to democratize AI and is the world's leading agentic AI company. We converge Generative and Predictive AI to help enterprises and public sector agencies develop purpose-built GenAI applications on their private data, with a strong focus on Sovereign AI for secure, compliant, and flexible deployments. Our open-source technology is trusted by over 20,000 organizations worldwide, including more than half of the Fortune 500. We partner with industry leaders like NVIDIA, Dell Technologies, and AWS, and have raised $256 million from top investors.
About This Opportunity
We are seeking a Senior AI Engineer to design and ship end-to-end AI solutions for complex enterprise problems in APAC, focusing on agentic AI systems, LLM applications, and production ML pipelines. This is a hands-on, customer-facing role embedded within a field team, where your work will directly impact real enterprises. You will collaborate with Kaggle Grandmasters, ML engineers, and domain experts to deliver AI solutions that drive measurable business outcomes.
What You Will Do
Customer Engagement Leadership
- Lead end-to-end technical engagements with enterprise customers, ensuring delivery quality and managing stakeholder relationships.
- Manage multiple concurrent engagement streams, coordinating workplans and milestones across cross-functional teams.
- Serve as the primary technical escalation point, proactively identifying risks and driving resolutions.
- Build trusted relationships with customer data science teams, engineering leads, and executive stakeholders.
- Lead pre-sales and proof-of-concept engagements, defining technical strategy and building enterprise trust through demonstrations.
- Represent H2O.ai externally at customer workshops and technical deep-dives.
Agentic AI & LLM Engineering
- Design and build agentic AI systems and multi-agent frameworks for automating complex 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.
- Stay current with the evolving agentic AI landscape and integrate best practices into customer engagements.
End-to-End AI Application Development
- Own the full development lifecycle: problem framing, data exploration, model development, API integration, and production deployment.
- Build scalable backend services and APIs to expose AI capabilities.
- Integrate AI models into customer environments (cloud, on-prem, hybrid), ensuring performance and stability.
- Develop ML pipelines and LLMOps infrastructure for continuous model improvement and monitoring.
Team Collaboration & Delivery Excellence
- Coordinate delivery across engineers, program managers, and solution architects.
- Set the technical bar for engagements, review outputs, and shape architecture decisions.
- Mentor and guide junior ML engineers and solution engineers.
- Collaborate with H2O.ai product and engineering teams to provide customer feedback and influence the roadmap.
What We Are Looking For
Experience & Background
- 8+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
- Experience leading technical delivery across complex, multi-stakeholder enterprise engagements.
- Demonstrable experience building LLM-powered applications (RAG pipelines, agentic workflows, fine-tuned models).
- Strong Python engineering skills with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex).
- 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.
- Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
- Solid grounding in classical ML for selecting appropriate tools.
- Backend development skills: REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications.
- Strong executive communication skills.
- Comfortable with ambiguity and setting direction for a team.
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 opportunities.
H2O.ai is committed to creating a diverse and inclusive culture. We encourage all qualified applicants to apply.
Key skills/competency
- AI Engineering
- LLM Applications
- Agentic AI Systems
- Machine Learning
- Python
- Cloud Deployment
- Customer Engagement
- Technical Leadership
- MLOps
- GenAI
Skills & topics
- AI Engineer
- Lead Engineer
- GenAI
- LLM
- Agentic AI
- Machine Learning
- Python
- Cloud
- Enterprise AI
- MLOps
How to get hired
- Tailor your resume: Highlight 8+ years of AI/ML engineering, LLM application development, and technical leadership in enterprise engagements.
- Showcase Python & ML skills: Emphasize proficiency in PyTorch, TensorFlow, scikit-learn, LangChain, and cloud deployment experience (AWS, Azure, GCP).
- Demonstrate customer-facing experience: Detail your experience leading technical engagements, managing stakeholders, and building client relationships.
- Prepare for technical interviews: Be ready to discuss GenAI concepts, classical ML, backend development, and production deployment strategies.
- Highlight leadership and communication: Prepare examples of mentoring junior engineers and communicating complex technical information to executives.
Technical preparation
Master Python, ML frameworks, and LLM tooling.,Build and deploy LLM applications (RAG, agents).,Develop backend services and AI pipelines.,Practice cloud deployment and MLOps.
Behavioral questions
Describe leading a complex enterprise AI project.,How do you handle ambiguity in requirements?,Share an experience mentoring junior engineers.,How do you build trust with executive stakeholders?
Frequently asked questions
- What are the key responsibilities for a Lead Forward Deployed Engineer AI at H2O.ai?
- As a Lead Forward Deployed Engineer AI at H2O.ai, you will lead end-to-end technical engagements with enterprise customers, design and build agentic AI systems and LLM applications, and own the full development lifecycle of AI solutions. You will also manage customer relationships, coordinate cross-functional teams, and mentor junior engineers.
- What technical skills are most important for this Lead Forward Deployed Engineer AI role at H2O.ai?
- Key technical skills include 8+ years of AI/ML engineering experience, strong Python proficiency, experience with ML frameworks (PyTorch, TensorFlow, scikit-learn), and LLM tooling (LangChain, LlamaIndex). Experience with cloud deployments (AWS, Azure, GCP, Kubernetes), RAG pipelines, agentic workflows, and MLOps is crucial.
- How does H2O.ai foster career growth for its AI engineers?
- H2O.ai offers career growth opportunities within a world-class team environment. They focus on providing market-leading total rewards, a flexible and remote-friendly culture, and the chance to work on cutting-edge AI technologies alongside top talent like Kaggle Grandmasters.
- What is the role of H2O.ai in the AI for Good initiative?
- H2O.ai's AI for Good program supports nonprofit groups, foundations, and communities in advancing education, healthcare, and environmental conservation. This reflects the company's strong ethos of using AI responsibly and for societal benefit.
- What is the expected experience level for a Lead Forward Deployed Engineer AI at H2O.ai?
- We are looking for candidates with 8+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment. Demonstrable experience leading complex, multi-stakeholder enterprise engagements is also essential.
- Does H2O.ai support remote work for this Lead Forward Deployed Engineer AI position?
- Yes, H2O.ai offers a remote-friendly culture and a flexible working environment, making this position accessible to qualified candidates regardless of their physical location, though the role is based in Dallas, Texas.
- What are some of the key technologies used by H2O.ai in their AI solutions?
- H2O.ai leverages a range of technologies including Generative AI, Predictive AI, agentic AI systems, LLM applications (RAG, fine-tuning, prompt engineering), ML pipelines, and MLOps. They also utilize ML frameworks like PyTorch and TensorFlow, and LLM tooling such as LangChain and LlamaIndex.