Job Specifications
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 - H2O.ai powers AI transformation for companies like AT&T, Commonwealth Bank of Australia, Singtel, Chipotle, Workday, Progressive Insurance, and NIH.
H2O.ai partners include Dell Technologies, Deloitte, Ernst & Young (EY), NVIDIA, Snowflake, AWS, Google Cloud Platform (GCP) and VAST. 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.
About This Opportunity
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.
Why H2O.ai?
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