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. 

What You Will Do 

ML System Implementation & Development 

• 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. 

Software Engineering & MLOps 

• 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.

What We Are Looking For

Core Programming & ML Fundamentals 

• 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). 

MLOps and Infrastructure 

• 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 (e.g., 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. 

Advanced Capabilities (Hands-On) 

• 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. 

Experience & Professional Skills 

• 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