9 days ago
Remote, IndiaSenior
Responsibilities
- Lead end-to-end technical engagements with enterprise customers, including delivery quality, stakeholder relationships, workplans, resourcing, milestones, and escalations.
- Build agentic AI systems, multi-agent frameworks, RAG applications, fine-tuned models, prompt-engineering workflows, function-calling integrations, and tool-use systems.
- Implement guardrails, evaluation frameworks, monitoring, and responsible AI controls for production-grade AI applications.
- 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 supporting continuous model improvement and production monitoring.
- Lead pre-sales, proof-of-concept engagements, customer workshops, executive briefings, and technical deep-dives.
- Coordinate engineers, program managers, and solution architects while setting technical direction and reviewing architecture and deliverables.
- Mentor junior ML engineers and solution engineers and collaborate with H2O.ai product and engineering teams on customer feedback and platform issues.
Requirements
- At least 5 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.
- 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.
- Understanding of prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, LLMOps, and classical machine learning.
- Backend development experience with REST APIs, Docker, Kubernetes, and CI/CD pipelines for AI applications.
- Ability to manage concurrent workstreams, coordinate cross-functional teams, communicate with executive and technical audiences, and set direction amid ambiguity.
- Preferred: Kaggle or competitive ML experience; familiarity with H2O.ai products, Wave, or H2O Document AI; regulated-industry AI deployment experience; 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 participation in a world-class team.
- Based in India and supporting enterprise customers across APAC.
- 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.