Senior MLOps Engineer I

About Us :

Zeitview is the leading intelligent aerial imaging company for high-value infrastructure, providing businesses with actionable, real-time insights to recover revenue, reduce risk and improve build quality. We serve customers in the solar, wind, insurance, construction, real estate, and critical infrastructure industries. Trusted by the largest enterprises in the world, Zeitview is active in over 70 countries. Our mission is to accelerate the global transition to renewable energy and sustainable infrastructure through advanced inspection solutions. Take a look at our latest achievements here!

About the Role :

As the Senior MLOps Engineer I, you will help turn the models built by our ML Scientists, Data Scientists, and Perception Engineers into reliable, production-grade services. You’ll work on the infrastructure, pipelines, and tooling that take a model or an LLM/agent-backed workflow from a research notebook to a fully monitored deployment running across multiple industry verticals, including our model registry, deployment pipelines, and the cloud infrastructure our AI/ML platform depends on.

This role sits at the intersection of R&D, Software Engineering, and DevOps. You will work daily with our R&D team to understand what a model needs to run in production (compute, data inputs, versioning, post-processing), and you’ll partner closely with the Platform and DevOps teams to provision the infrastructure, permissions, and deployment pathways that make it possible. You’ll also contribute to broader automation initiatives, helping provide the deployment visibility and pipeline reliability that let R&D, Software, Product, and Ops teams move in lockstep.

The day-to-day will include maintaining and extending our model registry, building and debugging deployment pipelines and cloud infrastructure, and setting up model and pipeline monitoring and testing. You will also troubleshoot issues, such as failed deployments, permissions errors, or inconsistent environments. You’ll also help shape and document standards for how models move from staging to production. Perhaps most importantly, you will serve as a key communicator ensuring R&D goals and challenges are well understood by Software Engineering and DevOps teams.

Responsibilities :

• Partner with Scientists: Work directly and iteratively with ML Scientists, Data Scientists, and Perception Engineers to translate experimental, research-oriented code into dependable, scalable production services without slowing down their research velocity.
• Cross-Functional Collaboration: Coordinate with DevOps and Software Engineering teams on infrastructure requests and shared data pipeline needs, and support broader automation initiatives and team goals.
• Model Registry, Deployment & Release Management: Maintain and improve model registry and deployment pipelines, and help implement safer release practices (e.g., shadow deployments, rollback procedures) to reduce risk.
• Cloud Infrastructure & CI/CD: Build, maintain, and troubleshoot cloud infrastructure and CI/CD pipelines that ML workloads run on, working closely with Engineering and DevOps teams on shared tooling, infrastructure-as-code, and cost optimization for compute-heavy workloads.
• Monitoring, Drift & Reproducibility: Implement monitoring and observability for models and pipelines in production, help R&D track model performance and drift over time, and support experiment tracking and dataset/model versioning.
• Ongoing Maintenance & Platform Support: Keep deployed ML systems healthy over time with dependency and infrastructure upgrades, capacity and cost management, data pipeline upkeep, and retraining or redeployment support, and extend support as needs evolve.
• Standards & Documentation: Help define and document conventions for model versioning, deployment promotion, and model documentation/lineage, and build tools to allow scientists and engineers to self-serve.

Qualifications: The following describes the qualifications for this position. Successful candidates are expected to meet most, but not all, of these requirements.

• Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related field; typically 4+ years of professional experience in MLOps, ML platform engineering, or infrastructure engineering supporting machine learning teams.
• Solid, applied knowledge of MLOps practices, with the ability to work independ…