Senior MLOps Engineer: Production ML Platform Lead

Zeitview (formerly DroneBase); Boston (MA); On-site; USD 170,000 - 180,000; Full time

Benefits offered by this job

Base salary $170,000–$180,000 USD

Target annual bonus

Stock options

Medical insurance with HSA

Dental and vision insurance

Unlimited PTO

Autonomy and upward mobility

Inclusive culture

Job summary

Zeitview in Boston seeks a Senior MLOps Engineer I to turn ML models into reliable, production-grade services. You will own infrastructure, pipelines, and tooling to move models from research notebooks to monitored deployments across multiple verticals, including model registry, deployment pipelines, and cloud infrastructure.

You will work at the intersection of R&D, Software, and DevOps, partnering with ML Scientists and Platform teams to provision infrastructure, permissions, and deployment

Qualifications

• 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; you communicate R&D goals and challenges to Software Engineering and DevOps teams.
• Strong Python skills and solid software engineering fundamentals (testing, code review, version control).
• Hands-on experience with a major cloud platform (e.g., AWS), IaC (Terraform), CI/CD tooling (Github Actions), and containerization/orchestration (Docker, Kubernetes).
• Experience building and operating production ML pipelines and model registries, including model versioning and safer release practices across environments, and coordinating cross-functional infrastructure projects.
• Experience building feedback loops from production back into training data, with experiment tracking and dataset/model versioning; familiarity with model documentation for reproducibility.
• 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, and token cost optimization.
• Nice to have: experience integrating relational databases and external APIs into production workflows.

Responsibilities

• Partner with Scientists: translate experimental code into dependable, scalable production services without slowing research velocity.
• Cross-Functional Collaboration: coordinate with DevOps and Software Engineering on infrastructure requests and data pipelines; support automation initiatives.
• Model Registry, Deployment & Release Management: maintain and improve model registry and deployment pipelines; implement safer release practices.
• Cloud Infrastructure & CI/CD: build and troubleshoot cloud infrastructure and CI/CD pipelines for ML workloads; cost optimization for compute-heavy workloads.
• Monitoring, Drift & Reproducibility: implement monitoring and observability for models and pipelines; track performance and drift; support experiment tracking and dataset/model versioning.
• Ongoing Maintenance & Platform Support: keep deployed ML systems healthy with upgrades, capacity and cost management, data pipeline upkeep, retraining or redeployment.
• Standards & Documentation: define and document conventions for model versioning, deployment promotion, and model documentation/lineage; build self-serve tooling.

Skills

Python

MLOps

AWS

Terraform

CI/CD

Docker

Kubernetes

Model registry

Canary deployments

Rollbacks

Experiment tracking

Versioning

Documentation

Education

Bachelor’s degree in CS/related field

Tools

Terraform

Github Actions

Docker

Kubernetes

AWS

Job description

Zeitview in Boston seeks a Senior MLOps Engineer I to turn ML models into reliable, production-grade services. You will own infrastructure, pipelines, and tooling to move models from research notebooks to monitored deployments across multiple verticals, including model registry, deployment pipelines, and cloud infrastructure.

You will work at the intersection of R&D, Software, and DevOps, partnering with ML Scientists and Platform teams to provision infrastructure, permissions, and deployment