1 day ago
Base Salary
$175k - $200k/yr
Responsibilities
- Lead end-to-end technical engagements with enterprise customers and remain accountable for delivery quality, stakeholder relationships, and outcomes.
- Coordinate multiple concurrent workstreams, workplans, resourcing, milestones, and cross-functional delivery teams.
- Serve as the primary technical escalation point and drive resolution across engineering, product, and leadership.
- Build trusted relationships with customer data science, engineering, and executive stakeholders while translating business needs into technical direction.
- Lead pre-sales, proof-of-concept engagements, customer workshops, executive briefings, and technical deep-dives.
- Design and build agentic AI systems, multi-agent frameworks, and LLM-powered applications using RAG, fine-tuning, prompt engineering, function calling, and tool use.
- Implement guardrails, evaluation frameworks, and responsible AI controls for reliable and safe production systems.
- Own the AI application lifecycle from problem framing and data exploration through model development, API integration, and production deployment.
- Build scalable backend services and APIs and integrate AI models into cloud, on-premises, and hybrid customer environments.
- Develop ML pipelines and LLMOps infrastructure for continuous model improvement and production monitoring.
- Review technical outputs, shape architecture decisions, maintain engineering quality, and mentor junior ML and solution engineers.
- Collaborate with H2O.ai product and engineering teams on customer feedback, roadmap input, and platform-level issues.
Requirements
- 8+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
- Demonstrated experience leading technical delivery across complex, multi-stakeholder enterprise engagements.
- Demonstrated experience building LLM-powered applications such as RAG pipelines, agentic workflows, or fine-tuned models.
- Strong Python engineering skills and experience with PyTorch, TensorFlow, scikit-learn, and LLM tooling such as LangChain or LlamaIndex.
- Experience deploying AI services in AWS, Azure, GCP, on-premises environments, or Kubernetes.
- Deep understanding of prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
- Strong backend development skills including REST APIs, Docker/Kubernetes containerization, and CI/CD pipelines for AI applications.
- Solid grounding in classical machine learning and the ability to select appropriate approaches for different problems.
- Ability to manage concurrent workstreams, coordinate cross-functional teams, set technical direction amid ambiguity, and communicate with executive and technical audiences.
- Preferred qualifications include Kaggle or competitive ML experience, familiarity with H2O.ai products, experience in regulated industries, exposure to tabular foundation models, AutoML, or enterprise ML platforms, and prior customer-facing or field engineering experience.
Benefits
- Remote-friendly culture and flexible working environment.
- Market-leading total rewards, career growth, and membership in a world-class team.
- Based in the San Francisco Bay Area; the posting is marked hybrid.
- H2O.ai is committed to a diverse and inclusive workplace.
Tech Stack
Categories
Forward Deployed
About H2O.ai
H2O.ai builds open-source and commercial platforms for machine learning and generative AI, used by enterprises and public-sector teams to develop models and applications on private data. Its portfolio includes H2O-3, Driverless AI, and tools for LLM fine-tuning and deployment, offered via subscriptions and cloud services. Founded in 2012 and headquartered in Mountain View, it is privately held; customers include AT&T, Commonwealth Bank of Australia, Workday, and the NIH.