ML Ops Engineer, AI

This is a fully remote position, open to applicants in United States.

📋 Description

• Conduct audits, enhance security, and optimize our current cloud infrastructure (AWS) to guarantee high availability, fault tolerance, and security for both training and production workloads.

• Design and sustain scalable architectures for deploying deep learning models (PyTorch/TensorFlow), focusing on low latency and high throughput in managing complex infrastructure data.

• Develop and oversee automated pipelines for model testing, validation, deployment, and rollback processes.

• Create efficient, scalable computing environments for training sophisticated computer vision and time-series models on extensive datasets.

• Establish thorough monitoring for model drift, data quality, and system health, ensuring swift responses to any performance declines.

⛳️ Requirements

• 4-6+ years of experience in MLOps, DevOps, or Data Engineering, with a significant focus on machine learning workloads.

• A mindset centered on security and stability—you proactively consider edge cases, failure modes, and system hardening.

• Strong collaborative skills to work closely with Data Scientists, facilitating seamless transitions from experimentation to production.

• Excellent communication abilities to convey architectural decisions and trade-offs to the wider technical team.

• In-depth knowledge of AWS (e.g., EC2, S3, EKS, SageMaker, Lambda) and cloud security best practices.

• Extensive experience with Docker and Kubernetes for packaging and scaling machine learning applications.

• Proficiency in tools such as Terraform or AWS CloudFormation.

• Experience in creating robust automated pipelines using GitHub Actions, GitLab CI, or Jenkins.

• Strong Python programming skills with an emphasis on writing clean, production-ready, and thoroughly 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