AI Engineer - Forward Deployed

h2o.ai; Chennai District; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

h2o.ai in India is seeking a Senior AI Engineer to design and ship end-to-end AI solutions for APAC enterprises, including agentic AI systems, LLM applications, and production ML pipelines.

You will work hands-on within a customer-facing field team, collaborating with ML engineers and domain experts to deliver production-ready AI that goes beyond demos and delivers measurable business outcomes. Based in India, you will influence deployments across cloud, on-prem, and hybrid environments.

Qualifications

Responsibilities

• Design and build agentic AI systems and multi-agent frameworks for enterprise customers.
• Develop and deploy LLM-powered applications using RAG, fine-tuning, prompt engineering, function calling, and tool use.
• Build production-ready applications independently: backend services, frontend interfaces, and the AI/ML layer connecting them.
• Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production-grade reliability and safety.
• Stay current with MCP, LLM orchestration frameworks, and bring the best of it into customer engagements.
• Rapidly build, iterate, and deploy predictive ML models (classification, regression, time-series forecasting, anomaly detection) for enterprise customers.
• Architect hybrid AI solutions that combine predictive ML with Generative AI (NL interfaces, RAG-based explanations, agentic orchestration).
• Own the full development lifecycle: 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 MLOps/LLMOps infrastructure that support continuous model improvement, automated retraining, drift detection, and monitoring in production.

Job description

Job Summary

We are looking for a Senior AI Engineer who builds things that matter. You will design and ship end-to-end AI solutions for some of APACs 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 India.

Responsibilities

Agentic AI LLM Engineering

• Design and build agentic AI systems and multi‑agent frameworks that automate complex, multi‑step workflows for enterprise customers.
• Develop and deploy LLM‑powered applications using techniques including RAG, fine‑tuning, prompt engineering, function calling, and tool use.
• Build production‑ready applications independently: backend services, frontend interfaces, and the AI/ML layer connecting them
• 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 of it into customer engagements.

Predictive AI Machine Learning

• Rapidly build, iterate, and deploy multiple predictive ML models (classification, regression, time‑series forecasting, anomaly detection) for enterprise customers
• Architect hybrid AI solutions that combine predictive ML (scoring, forecasting, classification) with Generative AI (natural language interfaces, RAG‑based explanations, agentic orchestration)

End‑to‑End AI Application Development

• Own the full development lifecycle: 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 (for both predictive models and LLM applications) and MLOps/LLMOps infrastructure that support continuous model improvement, automated retraining, drift detection, and monitoring in production.