Senior AI Engineer

About H2O.ai

H2O.ai is dedicated to democratizing AI for societal benefit, combining Generative and Predictive AI to empower enterprises and government bodies with customized AI agents, service-level models, and solutions using their private data. The company emphasizes secure, compliant, and flexible Sovereign AI solutions that uphold strict data privacy and control standards. Trusted by over 20,000 organizations globally, including over half of the Fortune 500, H2O.ai supports AI transformations for prominent companies such as AT&T, Commonwealth Bank of Australia, Wells Fargo, and more.

Role Overview

We are seeking a Senior AI Engineer passionate about creating impactful AI systems. This role involves designing and deploying comprehensive AI solutions to address some of the most intricate enterprise challenges in the APAC region, including building agentic AI frameworks, large language model (LLM) applications, and robust machine learning pipelines. Embedded within a customer-facing team, your contributions will be immediately visible and evaluated by real-world enterprise clients. You'll collaborate with Kaggle Grandmasters, machine learning engineers, and domain specialists to implement AI solutions that extend beyond prototypes to production and tangible business outcomes.

Key Responsibilities

• Develop model distillation methods and synthetic data generation techniques for fine-tuning open-source models such as Gemma, leveraging PEFT and QLORA frameworks.
• Create and implement agentic AI systems and multi-agent architectures designed to automate sophisticated, multi-step workflows for enterprise customers.
• Design and deploy LLM-driven applications utilizing retrieval-augmented generation (RAG), fine-tuning, prompt engineering, function calling, and tool integration.
• Build and maintain guardrails, evaluation frameworks, and apply responsible AI principles to ensure production-level reliability and safety.
• Continuously update knowledge on emerging agentic AI innovations including model composition protocols (MCP), LLM orchestration frameworks, and reasoning models to incorporate best practices in client projects.
• Own the entire AI application development lifecycle, from problem identification and data investigation to model creation, API deployment, and live production release.
• Construct scalable backend services and APIs to integrate AI capabilities within enterprise workflows and applications.
• Deploy AI models across diverse environments including cloud platforms, on-premises, and hybrid infrastructures, ensuring optimal performance, stability, and maintainability.
• Develop machine learning pipelines and large language model operations (LLMOps) infrastructure that facilitate continuous model enhancement and production monitoring.
• Engage directly with customers’ data scientists, engineers, and business leaders to translate complex real-world problems into AI-driven solutions.
• Support pre-sales activities and proof-of-concept demonstrations by rapidly delivering credible technical showcases to build trust.
• Communicate effectively across diverse audiences, delivering detailed technical presentations to engineering teams and outcome-centric updates to business stakeholders.
• Work collaboratively with Program Managers, Solution Engineers, and Kaggle Grandmasters to deliver coherent, high-impact AI solutions.

Qualifications and Expertise

• At least 3 years of practical experience in AI and machine learning engineering, including end-to-end model development and production rollout.
• Proven background in building applications powered by large language models, such as RAG pipelines, agentic workflow systems, and fine-tuned models.
• Strong proficiency in Python programming, with experience using machine learning frameworks including PyTorch, TensorFlow, and scikit-learn, as well as LLM toolkits such as LangChain and LlamaIndex.
• Experience deploying models and maintaining AI services in cloud environments like AWS, Azure, GCP, or on-premises Kubernetes clusters.
• Comprehensive understanding of contemporary Generative AI concepts, including prompt engineering, retrieval-augmented generation, fine-tuning strategies, reinforcement learning with human feedback (RLHF), model evaluation techniques, guardrails, and LLMOps practices.
• Solid foundation in classical machine learning enabling appropriate tool selection beyond the newest LLM trends.
• Backend development capabilities handling RESTful APIs, containerization with Docker and Kubernetes, and implementing CI/CD pipelines tailored for AI applications.
• Excellent problem-solving abilities with a comfort for ambiguity and capability to deliver quality engineering rapidly.
• Strong communication skills to articulate complex AI technologies to non-technical stakeholders clearly without oversimplification.

Additional Preferred Qualifications

• Competitive machine learning experience including participation in Kaggle competitions.
• Familiarity with H2O.ai’s latest products including TabH2O, TimeH2O, and Lightning-JEV.
• Experience deploying AI solutions in regulated sectors such as financial services or healthcare.
• Exposure to tabular foundation models, automated machine learning (AutoML), or enterprise machine learning platforms.
• Previous experience in a customer-facing or field engineering position.

Why Join H2O.ai?

• Recognized market leader offering competitive total rewards.
• Culture welcoming remote work and flexible schedules.
• Opportunity to join a world-class team with strong career advancement opportunities.

Diversity and Inclusion

H2O.ai is committed to fostering a diverse and inclusive workforce. All qualified candidates will be considered for employment without discrimination based on race, ethnicity, religion, gender, sexual orientation, age, disability, or any other protected category.