MLOps Engineer

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MLOps Engineer

Commitment:

24-months

Role Overview:

This role is responsible for deploying, monitoring, and maintaining Greenstand's machine learning models in production. The MLOps Engineer will ensure that ML pipelines, particularly an Airflow pipeline for species identification, are tested, optimized, and fully integrated into Greenstand's technology stack. This position plays a key role in ensuring Greenstand can process and analyze tree capture images effectively to track growth, species, and health.

Compensation:

Volunteer, until otherwise funded

Reports to:

Machine Learning Lead

Collaborates with:

Contributors, System users, Governmental agencies

Leads

ML Ops

AREAS of RESPONSIBILITY

• Deploy and maintain ML models, ensuring seamless integration with Greenstand’s image analysis workflows.

• Test and optimize Greenstand's Airflow pipeline for species identification, ensuring stability and efficiency.

• Work with backend teams to ensure resource allocation and infrastructure scaling for ML model execution.

• Automate machine learning workflows, enabling continuous integration and deployment (CI/CD) of models.

• Monitor performance of ML models in production and implement improvements as needed.

• Document workflows, processes, and best practices to ensure accessibility for future contributors.

• Identify and resolve bugs in ML pipelines, working closely with other developers and contributors.

• Use cloud-based solutions such as AWS or Google Cloud to support ML processing at scale.

• Collaborate with Greenstand leadership to ensure that ML deployment aligns with organizational goals.

QUALIFICATIONS/EXPERIENCE/KNOWLEDGE:

• Strong experience in MLOps, machine learning deployment, and backend integration.

• Expertise in Apache Airflow for managing ML workflows.

• Proficiency in Python and relevant ML libraries (TensorFlow, PyTorch, Scikit-learn, etc.).

• Experience with cloud platforms (AWS, Google Cloud, or Azure).

• Knowledge of containerization (Docker, Kubernetes) and CI/CD pipelines.

• Experience working with large-scale image datasets and ML pipelines is a plus.

• Strong problem-solving skills and ability to work both independently and collaboratively.