ML Ops Engineer

About SewerAI Corporation

SewerAI is transforming underground infrastructure management through AI-powered inspection and risk analysis. Our platform helps contractors, engineering firms, and utilities unlock valuable insights from sewer inspection data—turning hours of manual video review into actionable intelligence in minutes. After doubling our customer base over the past year, we’re now entering an exciting phase of accelerated growth.

About the Role

We're looking for an MLOps Engineer to own the Machine Learning Operations infrastructure that powers our AI products. In this role, you will be the architectural backbone of our machine learning systems, responsible for designing, hardening, and scaling the infrastructure that powers our applied machine learning models for underground infrastructure and sewer line analysis.

You will focus on transitioning research and development into robust, production-ready systems. This means taking ownership of our training and inference pipelines, fortifying our cloud-based architecture, and building seamless CI/CD processes to ensure our models deliver reliable, high-performing, and secure actionable insights for defect detection and infrastructure maintenance.

What You'll Work On

- Architectural Hardening: Audit, secure, and optimize our existing cloud infrastructure (AWS) to ensure high availability, fault tolerance, and security for both training and production workloads.

- Model Deployment & Inference: Design and maintain scalable architectures for serving deep learning models (PyTorch/TensorFlow), optimizing for low latency and high throughput in handling complex infrastructure data.

- CI/CD for Machine Learning: Build and maintain automated pipelines for model testing, validation, deployment, and rollback.

- Training Infrastructure: Architect efficient, scalable compute environments for training complex computer vision and time-series models on large datasets.

- Monitoring & Observability: Implement comp...