Contract jobs in Canada

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. Its open‑source technology is trusted by over 20,000 organizations worldwide – including more than half of the Fortune 500 – and powers AI transformation for companies such as AT&T, Commonwealth Bank of Australia, Singtel, Chipotle, Workday, Progressive Insurance, and NIH. H2O.ai partners with Dell Technologies, Deloitte, Ernst & Young (EY), NVIDIA, Snowflake, AWS, Google Cloud Platform (GCP) and VAST. The 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, H2O.ai aims to co‑create valuable AI applications for all users. The company has raised $256 million from investors including Commonwealth Bank, NVIDIA, Goldman Sachs, Wells Fargo, Capital One, Nexus Ventures and New York Life.

We are seeking a Senior ML Developer Consultant with strong software engineering skills and practical experience taking Machine Learning models to production. This role focuses on building, deploying and optimizing robust ML‑powered features and applications, requiring a hands‑on approach to MLOps, system integration and performance tuning. The ideal candidate will be a high‑contributing individual who can bridge the gap between Data Science prototypes and reliable, scalable production systems.

• End‑to‑End Pipeline Implementation: Implement and maintain end‑to‑end Machine Learning pipelines, focusing on robustness from data validation through to serving.
• Model Deployment: Deploy, integrate and maintain production ML models using the H2O MLOps framework, ensuring high reliability, low latency and efficient performance.
• Feature Integration: Develop efficient data processing pipelines and integrate models with existing data architectures (data warehouses, feature stores).
• Performance Optimization: Optimize model inference and system throughput for specific application requirements.

• Application Development: Build Python‑based APIs and microservices for real‑time and batch model prediction.
• MLOps Practices: Implement and enforce MLOps best practices, including continuous integration/continuous deployment (CI/CD), automated testing and proper model versioning.
• Monitoring: Set up and maintain monitoring for deployed models, tracking performance, data drift and system health.
• Infrastructure Collaboration: Work closely with infrastructure and platform teams to ensure optimal resource allocation and scalability on cloud platforms.

• Proficiency in Python: Expert‑level proficiency in Python and strong command of SQL. Working knowledge of C/C++ or Bash is a plus.
• ML Frameworks: Deep experience with key ML frameworks: TensorFlow, PyTorch, scikit‑learn, H2O3 and Driverless AI.
• Data Libraries: Extensive, hands‑on experience with core data processing libraries: NumPy, Pandas and Matplotlib.
• Application Development: Proven experience building applications and APIs using modern frameworks (e.g., Flask or FastAPI).

• Containerization & Orchestration: Strong practical experience with Docker and foundational knowledge of Kubernetes for deployment.
• Cloud Platforms: Experience deploying and operating ML workloads on at least one major cloud provider (AWS, GCP or Azure).
• Workflow Tools: Experience with ML workflow orchestration tools such as Airflow, Kubeflow or MLflow.
• Software Practices: Strong software development practices, including Git, unit testing, code review and experience with microservices architecture.

• Experience working with large language models (LLMs) or multimodal data (text, images, time‑series) in an applied setting, including experience with H2OGPTe.
• Familiarity with model serving patterns, auto‑scaling and resource management in a production context.
• Experience with performance profiling and basic model optimization techniques.

• Education & Experience: Master's degree in Computer Science, Engineering or a related technical field, plus 4+ years of professional experience building and deploying production software/ML systems.
• Problem‑Solving: Strong debugging, troubleshooting and analytical skills for diagnosing and resolving production system issues.
• Collaboration: Proven ability to collaborate effectively with data scientists and software engineers to transition experimental models into reliable production code.
• Delivery Focus: Track record of driving projects to completion and meeting strict performance requirements.

• Market leader in total rewards
• Remote‑friendly culture
• Flexible working environment
• Be part of a world‑class team
• Career growth
• This is a one‑year contract opportunity

H2O.ai is committed to creating a diverse and inclusive culture. All qualified applicants will receive consideration for employment without regard to their race, ethnicity, religion, gender, sexual orientation, age, disability status or any other legally protected basis.

H2O.ai is an innovative AI cloud platform company, leading the mission to democratize AI for everyone. Thousands of organizations from all over the world have used our cutting‑edge technology across a variety of industries. We’ve made it easy for people at all levels to generate breakthrough solutions to complex business problems and advance the discovery of new ideas and revenue streams. We push the boundaries of what is possible with artificial intelligence.

H2O.ai employs the world’s top Kaggle Grandmasters, the community of best‑in‑the‑world machine learning practitioners and data scientists. A strong AI for Good ethos and responsible AI drive the company’s purpose.

Please visit www.H2O.ai to learn more.