Aidashinc Staff MLOps Engineer at AiDASH will architect the operational backbone of the Data Inference AI Pipeline team. This hybrid role based in Palo Alto, CA involves building scalable, cost-effective ML model deployment infrastructure.
Responsibilities
Our satellite and AI-powered products are changing how utilities see and protect the grid, and we're gearing up for aggressive customer growth over the next two years. That growth runs on AI at scale. We're building a brand-new Data Inference AI Pipeline team to deliver it, and we're looking for a Staff MLOps Engineer to architect its operational backbone from the very first line of infrastructure code.
As the very first engineer on this team, you'll start with a blank canvas and a rare chance to build something that lasts. You'll shape how our models are deployed, how the pipeline scales and recovers under load, and what it costs to run. Just as importantly, you'll help define the culture, engineering standards, and ways of working that every future teammate inherits. This is a hands-on, high-leverage individual contributor role: your decisions will set the foundation the entire team builds on, and you'll be the go-to technical authority on MLOps as the team grows. You'll report to the Director of Engineering for the Data Inference AI Pipeline team.
Our pending acquisition by Schneider Electric will accelerate our mission as we bring our combined offering to our joint customer base, and the infrastructure you build will power outcomes at an even greater scale.
Location: This is a hybrid role based in Palo Alto, CA, requiring 2 days per week in the office.
How you'll make an impact:
Own the model deployment pipeline end-to-end: packaging, versioning, rollout, and rollback for ML models moving from training to production
Build and maintain deployment infrastructure on SageMaker (endpoints, batch transform, multi-model/multi-container hosting), and evaluate where alternatives such as self-hosted serving or other managed options are a better fit
Set the CI/CD pattern for model releases, including automated testing, staged rollout, and canary/shadow deployments
Scalability & elasticity
Design inference infrastructure that scales seamlessly with real-time and batch demand
Right-size compute (CPU/GPU/Inferentia or equivalent) for each model and workload to balance latency and cost
Load-test the pipeline and build capacity plans that stay ahead of customer growth milestones
Qualification
7-10+ years in software/ML engineeringHigh ownership mindsetExperience with large imageryWhat you'll loveComprehensive Medical
Required
7-10+ years in software/ML engineering, including 3+ years focused specifically on MLOps or ML infrastructure
Hands-on production experience with AWS SageMaker (or an equivalent managed ML platform) for model deployment and serving
A proven track record designing systems for elasticity/autoscaling, cost optimization, and failover/resilience at production scale
Strong expertise in containerization and orchestration (Docker, Kubernetes) and in CI/CD built for ML workloads, including model versioning and automated testing and rollout
Experience instrumenting and owning cost and performance metrics for the infrastructure you're responsible for
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
Preferred
Experience with large imagery, video, or 3D sensor and point-cloud data pipelines or other high-volume unstructured data at scale
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
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
What you'll love:
Comprehensive Medical, Dental, and Vision Coverage: 100% coverage for employees and 80% for their spouses and children
