About ShipIn

ShipIn Systems is redefining how the maritime industry understands, manages, and reduces operational risk. Our AI-powered visual fleet intelligence platform connects onboard video with shore-based teams, turning everyday vessel operations into actionable insight that helps prevent incidents before they escalate.

We work with many of the world’s leading shipowners and operators to bring greater visibility, accountability, and learning into daily operations at sea. The result is safer crews, stronger performance, and smarter decision-making across global fleets.

If you’re drawn to complex, real-world industries and want to build technology that changes behavior and improves safety at scale, join us.

About the Role

As an ML Ops Engineer at ShipIn, you will own the infrastructure and tooling that powers our entire computer vision development lifecycle- from collecting CCTV training data off vessels worldwide, through annotation, dataset versioning, model training, and deployment to ship-side edge devices.

You will also own the backend powering our AI-agent layer.

This is a high-leverage role: every detector the team ships flows through systems you build.

Key Responsibilities

• Design, build, and operate the end-to-end MLOps platform powering our computer vision development lifecycle- spanning data collection, annotation, dataset versioning, model training, and deployment to edge devices on vessel hardware.
• Build and operate the backend services for our AI-agent layer, enabling various agentic capabilities at fleet scale.
• Partner cross-functionally with CV engineers, data operations, and DevOps to ship reliable detectors to vessels worldwide at increasing velocity.
• Continuously raise the reliability, observability, and cost-efficiency bar across our ML infrastructure as the platform scales.
• Be updated with the latest technologies in your domain of expertise.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related technical field.
• Minimum of 5 years of hands-on experience in MLOps, Data Engineering, or AI Engineering.
• Proven experience operating data, training, and deployment pipelines in production.
• Familiarity with MLOps tooling such as MLflow, Weights & Biases, DVC, Airflow, or equivalents.
• Proven track record developing agentic AI flows and backend services powering LLM-based agents in production.
• Strong proficiency in Python and SQL, with the ability to write clean, maintainable, and scalable code for large, long-lived projects.
• Deep hands-on experience with cloud infrastructure (AWS preferred), Docker, and CI/CD.
• Experience with annotation platforms and edge model deployment is a plus.
• Strong systems thinking, root-cause analysis, and communication skills.