Senior DevOps Engineer (Infrastructure & MLOps)

Job Overview

Senior DevOps Engineer (Infrastructure & MLOps) Location Remote Employment Type Full time Location Type Remote Department Engineering Compensation $180K – $200K

Key Responsibilities

• Design, implement, and manage highly available infrastructure for our cloud-based platforms (AWS).
• Work with our internal engineering teams to architect and support AI/ML infrastructure, specifically managing AWS Lambda infrastructure, and some legacy SageMaker environments for model training, hosting, and inference.
• Create and automate robust deployment pipelines using CI/CD tools (GitLab / GitHub Actions) for both web applications and machine learning models.
• Build, maintain, and scale containerized applications with Docker and ECS/Fargate.
• Implement MLOps best practices to streamline the transition of models from development to production.
• Ensure system scalability and reliability through proactive monitoring, logging, and automated alerting.
• Collaborate with both Product Engineers and Data Scientists to optimize performance, security, and infrastructure costs.
• Manage and evolve our Infrastructure as Code (IaC) footprint.

Required Qualifications

• 5+ years of experience in a DevOps or infrastructure role.
• Expert knowledge of cloud platforms such as AWS, GCP and Azure
• Strong experience with containerization technologies (Docker, ECS / Kubernetes).
• Proven track record of designing and managing complex CI/CD pipelines.
• Experience with MLOps workflows (model versioning, retraining pipelines, or feature stores).
• Hands-on experience with monitoring and logging tools (Datadog, Prometheus, Grafana, MLflow).
• Expertise in scripting languages (Python is a must, along with Bash, Go, etc.).
• Proficiency with infrastructure automation tools (Terraform, Ansible, or CloudFormation).
• Excellent communication skills and the ability to bridge the gap between traditional DevOps and Data Science teams.

Preferred Skills

• Specific experience in AWS Lambda, SageMaker and other related AI/ML services.
• Experience with database management (SQL/NoSQL) and data pipeline orchestration, including Google BigQuery
• Knowledge of security best practices specifically regarding data privacy in AI and healthcare (HIPAA compliance).
• Previous experience in fast-paced, agile startup environments.

Benefits

Competitive salaries
Remote/hybrid environment
Potential equity compensation for outstanding performance
Flexible PTO
Company-wide sponsored lunches
Company paid disability and life insurance benefits
Company paid family and medical leave
Medical, dental, and vision insurance benefits
Discounted pet insurance
FSA/DCA and commuter benefits
401k
Credits for online fitness classes/gym memberships
Recovery suite at HQ – includes a cold plunge, sauna, and shower