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Data & ML Ops Lead
Gravis Robotics. Own the MLOps technical vision and roadmap, aligning infrastructure investments and architecture decisions with company and engineering milestones .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in MLOps, including the design and management of high-throughput data ingestion pipelines and cloud infrastructure. Proven ability to lead engineering teams, mentor talent, and implement CI/CD pipelines for machine learning workloads.
Highest-signal resume keywords
MLOps Technical VisionKubernetes Infrastructure ManagementData Pipeline OptimizationModel Lifecycle ManagementCI/CD Pipeline Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
MLOpsData EngineeringMachine Learning InfrastructureCloud InfrastructureData Pipeline ArchitectureModel RegistryArtifact VersioningExperiment TrackingInfrastructure as CodeHigh-Performance Data Retrieval
Soft Skills
Technical LeadershipMentoringTeam DevelopmentCollaborationContinuous Feedback
Tools & Technologies
AWSGCPAzureMLflowW&BDVCGitHub ActionsGitLab CIPrometheusGrafana
Certifications & Qualifications
Bachelor's DegreeMaster's Degree
Industry Keywords
High-Throughput Data IngestionMultimodal DatasetsEdge DevicesHybrid CloudOn-Premise Compute Clusters
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformGrafanaKubernetesPrometheus
About the role
Key responsibilities & impact- Own the MLOps technical vision and roadmap, aligning infrastructure investments and architecture decisions with company and engineering milestones
- Lead and build an engineering team architecting, building, and optimizing high-throughput data ingestion pipelines and platform infrastructure for petabyte-scale multimodal datasets
- Mentor and grow team members through continuous feedback, career development, and technical guidance
- Design, build, and operate high-availability hybrid cloud and on-premise compute clusters
- Provide developers and researchers with a seamless, unified compute experience
- Lead continuous deployment and monitoring pipelines for ML models deployed across thousands of edge devices in the field
- Establish full model lifecycle management, including model registries, artifact versioning, automated regression testing, and real-time observability and monitoring
- Collaborate with robotics engineers to translate requirements into reliable, scalable training environments
- Evaluate and integrate MLOps tooling on cloud and on-premise compute platforms
Requirements
What you’ll need- Bachelor's or Master's degree in Computer Science, Data Engineering, Electrical Engineering, or a related field
- 7+ years of hands-on experience in ML Ops, data engineering, or ML infrastructure roles, preferably in a leadership role driving ML Infrastructure
- 5+ years of production experience with Kubernetes, including designing and maintaining cloud infrastructure (AWS, GCP, or Azure) and on-premise cluster infrastructure
- Prior experience in technical leadership or engineering management, including mentoring and growing engineers
- Strong background in building, scaling, and optimizing large-scale data pipeline infrastructure for complex multimodal datasets and high-volume data, and organizing curated datasets
- Proven experience deploying, observing, and maintaining ML models at scale across thousands of edge/remote devices in production
- Deep experience with model registry, artifact versioning, experiment tracking, and data versioning tools such as MLflow, W&B, and DVC
- Demonstrated track record of building CI/CD pipelines for ML workloads, such as GitHub Actions and GitLab CI, implemented with Infrastructure as Code
- Experience with robotics data, including point clouds, camera streams, and timeseries data (nice-to-have)
- Experience with robotics and DevOps tooling such as Foxglove, Prometheus, and Grafana (nice-to-have)
- Experience with databases (nice-to-have)
- Expertise with large-scale database architectures, indexing frameworks, and high-performance data retrieval (nice-to-have)
Benefits
Comp & perks- Equal opportunity employer committed to building an inclusive and diverse team
- Inclusive, non-discriminatory workplace