Staff MLOps Engineer

Join our team as a Staff MLOps Engineer and be a part of a groundbreaking mission to revolutionize how utilities protect the grid. As the first engineer on our new Data Inference AI Pipeline team, you'll have the unique opportunity to build the operational backbone of our AI at scale initiative from the ground up. Your decisions will shape the future of our models, pipeline scalability, and cost efficiency. This is a hands-on, high-leverage individual contributor role where you'll be the go-to technical authority on MLOps as the team grows.

About AiDash

• A proven track record designing systems for elasticity/autoscaling, cost optimization, and failover/resilience at production scale
• Hands-on production experience with AWS SageMaker (or an equivalent managed ML platform) for model deployment and serving

Responsibilities

• Architect the operational backbone of the Data Inference AI Pipeline team, shaping how models are deployed and how the pipeline scales.
• Own the model deployment pipeline end-to-end, including packaging, versioning, rollout, and rollback for ML models moving from training to production.
• Design inference infrastructure that scales seamlessly with real-time and batch demand, and own the cost-per-inference metric.
• Strong expertise in containerization and orchestration (Docker, Kubernetes) and in CI/CD built for ML workloads, including model versioning and automated testing and rollout
• The ability to set technical direction as a Staff-level IC, building buy-in across Data Science and other engineering teams
• High ownership mindset, low ego, and a collaborative spirit, with a passion for building platforms that help others ship faster
• Experience with multimodal data (imagery, sensor/time-series, geospatial) beyond purely textual or tabular data
• Experience building MLOps practices from scratch on a new team

Requirements

• 7-10+ years in software/ML engineering, including 3+ years focused specifically on MLOps or ML infrastructure
• Experience instrumenting and owning cost and performance metrics for the infrastructure you're responsible for
• Experience with large imagery, video, or 3D sensor and point-cloud data pipelines or other high-volume unstructured data at scale

Preferred

• Familiarity with model monitoring and drift-detection tooling, and GPU cost-optimization techniques (quantization, batching, Inferentia/Trainium or equivalent)
• A background in a regulated or enterprise B2B domain such as utilities, energy, infrastructure, healthcare, or finance
• Relevant AWS/Kubernetes certifications or open-source MLOps tooling contributions

Pay

USD 230,000 - 270,000