Staff MLOps Engineer

Sourced from feeny.ai

AiDASH is building a new Data Inference AI Pipeline team and seeks a Staff MLOps Engineer to architect its operational backbone from scratch. As the first engineer on the team, you will own model deployment, inference scalability, cost-per-inference, and failover/resilience for satellite and AI-powered grid intelligence products. This is a hands-on staff-level IC role reporting to the Director of Engineering, based hybrid in Palo Alto, CA.

• Own the end-to-end model deployment pipeline: packaging, versioning, rollout, and rollback
• Build and maintain deployment infrastructure on AWS SageMaker and evaluate serving alternatives
• Set CI/CD patterns for model releases with automated testing, staged rollout, and canary/shadow deployments
• Design inference infrastructure that scales with real-time and batch demand
• Right-size CPU/GPU/Inferentia compute to balance latency and cost
• Own and drive down the cost-per-inference metric
• Design failover and disaster recovery across regions and availability zones
• Build monitoring, alerting, and on-call runbooks for model-serving infrastructure
• Partner with Data Science and Web Application teams to bring models to production

• 7-10+ years in software/ML engineering, including 3+ years focused on MLOps or ML infrastructure
• Hands-on production experience with AWS SageMaker or an equivalent managed ML platform
• Proven track record designing for elasticity/autoscaling, cost optimization, and failover/resilience at scale
• Strong expertise in Docker, Kubernetes, and CI/CD for ML workloads
• Experience instrumenting and owning cost and performance metrics
• Ability to set technical direction as a Staff-level IC and build cross-team buy-in

• Experience with large imagery, video, 3D sensor, or point-cloud data pipelines
• Experience with multimodal data (imagery, sensor/time-series, geospatial)
• Experience building MLOps practices from scratch on a new team
• Familiarity with model monitoring, drift detection, and GPU cost optimization (quantization, batching, Inferentia/Trainium)
• Background in regulated or enterprise B2B domains such as utilities, energy, or infrastructure
• Relevant AWS/Kubernetes certifications or open-source MLOps contributions