Note: The job is a remote job and is open to candidates in USA. SewerAI is transforming underground infrastructure management through AI-powered inspection and risk analysis. They are seeking an MLOps Engineer to own the Machine Learning Operations infrastructure that powers their AI products, focusing on designing and scaling systems for machine learning models in sewer line analysis.

Responsibilities

• 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 comprehensive monitoring for model drift, data quality, and system health, ensuring rapid response to performance degradation

Skills

• Deep expertise in AWS (e.g., EC2, S3, EKS, SageMaker, Lambda) and cloud security best practices
• Strong experience with Docker and Kubernetes for packaging and scaling ML applications
• Proficiency with tools like Terraform or AWS CloudFormation
• Experience building robust automated pipelines using GitHub Actions, GitLab CI, or Jenkins
• Strong Python skills with a focus on writing clean, production-grade, and well-tested code
• Familiarity with model registry and tracking tools (e.g., MLflow, Weights & Biases)
• 4-6+ years of experience in MLOps, DevOps, or Data Engineering, with a strong emphasis on machine learning workloads
• A security-first and stability-first mindset—you think about edge cases, failure modes, and system hardening by default
• Strong collaborative instincts to work closely with Data Scientists, ensuring smooth handoffs from experimentation to production
• Clear communication skills to articulate architectural decisions and tradeoffs to the broader technical team
• Experience with our specific data stack (Hex, dbt, ClickHouse, Anyscale, Ray, Deeplake)
• Familiarity with deep learning frameworks (PyTorch preferred) and optimization techniques like TensorRT or ONNX
• Knowledge of edge computing or deploying models to IoT devices
• Experience in the infrastructure, utility, or geospatial domains

Benefits

• Equity opportunities available
• Medical, Dental, Vision, Basic Life, 401(k), and more
• Unlimited PTO
• Tools and resources to support success
• Competitive compensation with high-growth potential

Company Overview

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