Senior MLOps Engineer

Zeitview

Senior MLOps Engineer turning models into reliable services for Zeitview, an intelligent aerial imaging company. Working on infrastructure, pipelines, and tooling for production-grade deployments.

Posted 7/21/2026full-timeRemote • Massachusetts • 🇺🇸 United StatesSenior💰 $170,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in MLOps practices, including building and operating production ML pipelines, model registries, and deployment strategies. Proficient in Python and cloud platforms, with a strong foundation in software engineering principles and infrastructure management.

Highest-signal resume keywords

MLOps PracticesPython ProgrammingCloud Platform ExperienceCI/CD ToolingModel Registry Management

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Hard Skills

MLOpsProduction ML PipelinesModel VersioningInfrastructure-as-CodeTestingCode ReviewVersion ControlData Pipeline DevelopmentExperiment TrackingContainerization

Soft Skills

Problem SolvingCollaborationCommunication

Tools & Technologies

AWSTerraformGithub ActionsDockerKubernetesPostgreSQLGraphQLHasura

Industry Keywords

Machine LearningModel DeploymentGeospatial MLComputer VisionLLM Systems

Tech Stack

Tools & technologies

AWSCloudDockerGraphQLKubernetesPostgresPythonTerraform

About the role

Key responsibilities & impact

Help turn the models built by our ML Scientists, Data Scientists, and Perception Engineers into reliable, production-grade services.
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.
Partner daily with the R&D team to understand what a model needs to run in production (compute, data inputs, versioning, post-processing).
Coordinate with the Platform and DevOps teams to provision the infrastructure, permissions, and deployment pathways.
Maintain and extend the model registry, build and debug deployment pipelines and cloud infrastructure, and set up model and pipeline monitoring and testing.
Troubleshoot issues, such as failed deployments, permissions errors, or inconsistent environments.
Help shape and document standards for how models move from staging to production.
Ensure R&D goals and challenges are well understood by Software Engineering and DevOps teams.

Requirements

What you’ll need

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 independently across varied production scenarios and escalate only genuinely complex or ambiguous problems.
Demonstrated experience working directly with researchers or ML scientists.
Strong Python skills and solid software engineering fundamentals (testing, code review, version control)
Hands-on experience with a major cloud platform (e.g., AWS), infrastructure-as-code (Terraform), CI/CD tooling (Github Actions), and containerization/orchestration (e.g., Docker, Kubernetes)
Experience building and operating production ML pipelines and model registries, including model versioning and safer release practices (e.g., canary deployments, rollbacks) across environments
Experience building feedback loops from production back into training data, capturing human corrections as labels and turning retraining into a repeatable pipeline.
Familiarity with experiment tracking, dataset/model versioning, and model documentation practices that support reproducible, auditable ML workflows is a plus.
Familiarity with computer vision or geospatial ML pipelines [Nice to have]
Experience operating LLM/Agentic systems in production, evaluation harness, prompt/tool/retrieval versioning, tracing, token cost optimization [Nice to have]
Experience building data pipelines against relational databases (e.g. PostgreSQL) and API/GraphQL data layers (e.g., Hasura), and integrating external/third-party APIs into production workflows.

Benefits

Comp & perks

Your choice of multiple medical insurance plans, including options with an HSA and 100% coverage of the premium for yourself and your dependents
100% paid dental and vision insurance
Unlimited PTO: We mean it when we say we prioritize work-life balance and mental health
Autonomy and upward mobility
Diverse, equitable, and inclusive culture: a place where your voice matters