Who you are

• 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 (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
• 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

What the job involves

• 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 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
• This is a one year contract opportunity

Benefits

• Remote-friendly culture
• Flexible working environment
• Career growth