Machine Learning Operations Engineer

Job Description

Utilidata is a fast-growing energy technology company using distributed artificial intelligence (AI) to accelerate the clean energy transition and better serve utility customers. As the electric grid becomes more complex with the rapid increase of electric vehicles, distributed solar, batteries, heat pumps and extreme weather, utilities need real-time visibility of grid conditions. Utilidata’s distributed AI platform, called Karman, is powered by our custom NVIDIA module and will transform the way utility companies operate with real-time visibility at the grid edge to better utilize customer energy resources, reduce power outages, and enable quicker storm recovery.

We are seeking a skilled Machine Learning Operations (MLOps) Engineer to join our team. The ideal candidate will play a crucial role in bridging the gap between our software-development, data-management, and AI-modeling teams. This role will be responsible for ensuring seamless implementation of ML development processes, and deployment of machine learning models into production on our distributed / IoT devices. We are looking for candidates who are collaborative, adaptive and mission-driven. This is a remote position based in the United States. Candidates will be expected to collaborate cross-functionally with remote teams based across the country.

Responsibilities

Work collaboratively with Cloud teams to design and build data infrastructure/pipelines to support ML model development workflows

Develop and maintain MLOps infrastructure to automate model training, testing, deployment, monitoring, model provenance, and version control

Collaborate with ML modelers, data scientists, and software developers to implement best practices in continuous integration, continuous deployment (CI/CD), and version control for data analytics and machine-learning systems

Work with AI developers to design and build LLM workflows for fine-tuning, distillation, and system evaluation

Foster a culture of open communication, innovation, and continual improvementMinimum Qualifications

8+ years of professional experience including 5+ years of proven experience in data engineering architecture and implementation for ML workflows

3+ years of proven experience in MLOps, DevOps, or related field, with a strong understanding of machine-learning lifecycle-management

Experience with data pipelines for ML workflows

Experience with big data distributed processing such as SPARK, DASK, or RAY

Experience with MLOps frameworks

Proficiency in CI/CD tools, containerization technologies (Docker, Kubernetes), and cloud services (AWS, Azure, GCP)

Excellent collaboration and communication skills to work effectively across teamsEnhanced Qualifications (Nice to Have)

Advanced degree in Computer Science, Engineering, or another related field

Experience with data infrastructure and ML Ops for distributed / IoT systems

Experience with LLM development workflows including training, deploying, and model management

Experience with time-series datasets

Experience with data science and algorithms development Salary Range: $140,000 to $170,000 depending on experience

Location: This position can be performed remotely from anywhere in the United States.

Our Commitments:

Utilidata values the diversity of our team. We provide equal employment opportunities without regard to race, color, religion, creed, sex, gender, sexual orientation, gender identity or expression, national origin, age, physical disability, mental disability, medical condition, pregnancy or childbirth, sexual orientation, genetics, genetic information, marital status, or status as a covered veteran or any other basis protected by applicable federal, state and local laws.

We are committed to:

Creating a diverse and inclusive workplace that is welcoming, supportive, affirming and respectful

Empowering employees to solve problems and work together to make a difference

Providing mentorship and growth opportunities as part of a collaborative team

A flexible work environment with flexible paid time off

Competitive compensation and benefits, including health, dental, vision, and employer-match 401k

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Full-time

Hybrid remote