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H2O.ai · 2 days ago

Machine Learning Developer, Consultant

Canada

Contract

Remote

Mid, Senior Level

4+ years exp

H2O.ai is a leading AI company dedicated to democratizing AI through innovative solutions. They are seeking a Senior ML Developer Consultant to implement and optimize machine learning systems, focusing on deploying ML models and integrating them into existing architectures.

AnalyticsArtificial Intelligence (AI)Machine LearningSoftware

Hiring Manager

Gina Padilla

Responsibilities

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 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

Qualification

Machine LearningPythonMLOpsCloud PlatformsSQLTensorFlowPyTorchDockerGitDelivery FocusCollaborationProblem-Solving

Required

Expert-level proficiency in Python and strong command of SQL

Working knowledge of C/C++ or Bash is a plus

Deep experience with key ML frameworks: TensorFlow, PyTorch, Scikit-learn, H2O3, and Driverless AI

Extensive, hands-on experience with core data processing libraries: NumPy, Pandas, and Matplotlib

Proven experience building applications and APIs using modern frameworks (e.g., Flask or FastAPI)

Strong practical experience with Docker and foundational knowledge of Kubernetes for deployment

Experience deploying and operating ML workloads on at least one major cloud provider (e.g., AWS, GCP, or Azure)

Experience with ML workflow orchestration tools such as Airflow, Kubeflow, or MLflow

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

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

Strong debugging, troubleshooting, and analytical skills for diagnosing and resolving production system issues

Proven ability to collaborate effectively with data scientists and software engineers to transition experimental models into reliable production code

Track record of driving projects to completion and meeting strict performance requirements