Software Engineer, AI Engineer, Machine Learning​/ ML Engineer

Founded in 2012, H2O.ai is on a mission to democratize AI.

As the world’s leading agentic AI company, H2O.ai converges Generative and Predictive AI to help enterprises and public sector agencies develop purpose-built GenAI applications on their private data.

With a focus on Sovereign AI—secure, compliant, and infrastructure-flexible deployments—H2O.ai delivers solutions that align with the highest standards of data privacy and control.

Our open-source technology is trusted by over 20,000 organizations worldwide, including more than half of the Fortune 500.

H2O.ai powers AI transformation for companies like AT&T, Commonwealth Bank of Australia, Chipotle, Workday, Progressive Insurance, and NIH.

H2O.ai partners include NVIDIA, Dell Technologies, Deloitte, Ernst & Young (EY), Snowflake, AWS, Google Cloud Platform (GCP), VAST Data and MinIO.

H2O.ai’s AI for Good program supports nonprofit groups, foundations, and communities in advancing education, healthcare, and environmental conservation.

With a vibrant community of 2 million data scientists worldwide, H2O.ai aims to co-create valuable AI applications for all users.

H2O.ai has raised $256 million from investors, including Commonwealth Bank, NVIDIA, Goldman Sachs, Wells Fargo, Capital One, Nexus Ventures and New York Life.

For more information, visit (Use the "Apply for this Job" box below). .

About This Opportunity H2O.ai is seeking a highly motivated Full Stack & Systems Software Engineer to join our H2

OGPTe generative AI project team.

This is a hands-on technical role for someone who thrives on solving complex problems, moves fast, and refuses to accept "no" as an answer.

You'll be working at the intersection of cutting-edge generative AI and production systems, delivering end-to-end solutions that push the boundaries of what's possible.

This is a hybrid position is based in Ottawa, Canada.

What You Will Do Design, develop, and deploy full stack features for the H2

OGPTe platform from concept to production.

Build and maintain robust systems software using Python, Go, and React.

Implement and optimize solutions across major cloud platforms (AWS, Azure, GCP).

Own the complete development lifecycle including CI/CD pipelines, testing, deployment, and monitoring.

Collaborate closely with ML researchers and engineers to productionize generative AI innovations.

Debug complex distributed systems issues and deliver creative solutions under pressure.

Contribute to architectural decisions that shape the future of our generative AI platform.

Rapidly prototype new capabilities and iterate based on user feedback.

What We Are Looking For Required Qualifications 2+ years of professional software engineering experience.

Strong programming skills in Python, Go, and React (or demonstrated ability to quickly master new technologies).

Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native architectures.

Hands-on experience with CI/CD pipelines and Dev Ops practices.

Proven ability to deliver features end-to-end, from design through deployment.

Track record of learning quickly and adapting to new technologies and domains.

Essential Attributes Can-do attitude: You see obstacles as puzzles to solve, not roadblocks.

Ownership mentality: You take pride in delivering high-quality solutions and stand behind your work.

Fast learner: You're energized by new challenges and technologies, especially in ML and generative AI.

Team player: You collaborate effectively, share knowledge, and elevate those around you.

Execution-focused: You bias toward action and deliver results consistently.

Innovative mindset: You're excited about pushing boundaries in generative AI applications.

Tech Stack

Languages:

Python, Go, React/JavaScript Cloud: AWS, Azure, GCP Domain: Generative AI, LLMs, ML infrastructure Tools: CI/CD pipelines, containerization, distributed systems How to Stand Out From the Crowd Experience with machine learning systems, frameworks, or infrastructure.

Background in generative AI, LLMs, or related ML technologies.

Familiarity with containerization (Docker, Kubernetes) and microservices architectures.

Experience with scalable distributed systems and data processing pipelines.

Open source contributions or personal projects…