Description:

Utilidata is a fast-growing energy technology company focused on using distributed artificial intelligence (AI) to enhance the clean energy transition and improve utility customer service.
The company’s AI platform, Karman, provides real-time visibility of grid conditions to help utility companies manage the complexities of the electric grid.
The role of Machine Learning Operations (MLOps) Engineer involves bridging the gap between software development, data management, and AI modeling teams.
Responsibilities include designing and building data infrastructure for ML workflows, maintaining MLOps infrastructure, collaborating on best practices for CI/CD, and fostering a culture of communication and innovation.
This is a remote position based in the United States, requiring collaboration with cross-functional teams across the country.

Requirements:

Candidates must have 8+ years of professional experience, including 5+ years in data engineering architecture and implementation for ML workflows.
A minimum of 3+ years of experience in MLOps, DevOps, or a related field, with a strong understanding of machine-learning lifecycle management is required.
Experience with data pipelines for ML workflows and big data distributed processing tools such as SPARK, DASK, or RAY is necessary.
Proficiency in MLOps frameworks, CI/CD tools, containerization technologies (Docker, Kubernetes), and cloud services (AWS, Azure, GCP) is essential.
Excellent collaboration and communication skills are required to work effectively across teams.

The salary range for this position is $140,000 to $170,000, depending on experience.
Utilidata offers a flexible work environment with flexible paid time off.
The company provides competitive compensation and benefits, including health, dental, vision, and employer-match 401k.
Utilidata is committed to creating a diverse and inclusive workplace, empowering employees, and providing mentorship and growth opportunities.