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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. Required Technical Skills
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Cloud Infrastructure: Deep expertise in AWS (e.g., EC2, S3, EKS, SageMaker, Lambda) and cloud security best practices. - Containerization & Orchestration: Strong experience with Docker and Kubernetes for packaging and scaling ML applications. - Infrastructure as Code (IaC): Proficiency with tools like Terraform or AWS CloudFormation. - CI/CD Pipelines: Experience building robust automated pipelines using GitHub Actions, GitLab CI, or Jenkins. - Programming: Strong Python skills with a focus on writing clean, production-grade, and well-tested code. - MLOps Frameworks: Familiarity with model registry and tracking tools (e.g., MLflow, Weights & Biases). Nice-to-Have Skills
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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. What We're Looking For
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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. What You'll Gain
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Impact: Your infrastructure will directly support systems that prevent critical failures in city utility networks. - Ownership: You will have the autonomy to shape the foundational MLOps architecture and set the standard for engineering excellence on the AI team. - Modern Stack: Work alongside highly skilled peers in a modern data and ML ecosystem. Compensation & Benefits
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Base Salary: $130,000-160,00
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Equity opportunities available
