Senior ML Developer, Consultant

Senior ML Developer, Consultant
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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 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:
Tensor Flow, PyTorch, Scikit‑learn, H2O3, and Driverless AI.

Data Libraries:
Extensive, hands‑on experience with core data processing libraries:
Num Py, 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…