Job Description:

• Audit, secure, and optimize our existing cloud infrastructure (AWS) to ensure high availability, fault tolerance, and security for both training and production workloads.
• Design and maintain scalable architectures for serving deep learning models (PyTorch/TensorFlow), optimizing for low latency and high throughput in handling complex infrastructure data.
• Build and maintain automated pipelines for model testing, validation, deployment, and rollback.
• Architect efficient, scalable compute environments for training complex computer vision and time-series models on large datasets.
• Implement comprehensive monitoring for model drift, data quality, and system health, ensuring rapid response to performance degradation.

Requirements:

• 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.
• 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).

Benefits:

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