DevOps Engineer, Surveillance

What You'll Do

• Lead the design and build of scalable data infrastructure and pipelines to power surveillance analytics, enabling rapid exploration and production deployment of signals and models
• Build and operate infrastructure as code to provision, secure, and manage cloud resources and platform services with an emphasis on repeatability and automation
• Develop and maintain containerized platforms and orchestration layers to run analytics and machine learning workloads reliably and efficiently at scale
• Implement and evolve observability, logging, and alerting across the stack to shorten time to detection and resolution for production incidents
• Automate security controls, access management, and compliance checks within platform tooling to support regulated surveillance workflows
• Mentor engineers and contribute to platform best practices, documentation, and runbooks to drive continuous improvement and team impact
• Optimize cloud cost, performance, and operational practices for large-scale data processing, storage, and model training workloads

What’s Required

• Bachelor's degree in computer science, engineering, or a related technical field, or equivalent professional experience
• 5+ years of hands-on experience in cloud infrastructure, DevOps, or platform engineering supporting production data or machine learning workloads
• Proven, hands-on experience with data warehouses and lakehouses such as Snowflake, Redshift, BigQuery, or Databricks
• Strong command of Infrastructure as Code with Terraform and Terraform Enterprise
• Proficiency in containerization with Docker or Podman, orchestration using Kubernetes, AWS EKS, or ECS Fargate, and implementation of GitOps principles and workflows
• Hands-on experience building CI/CD pipelines using GitHub Actions and automating model lifecycle using MLflow, Kubeflow, or Weights & Biases
• Experience operating observability and monitoring stacks including Datadog, AWS CloudWatch, and the Grafana ecosystem (Grafana, Loki, Prometheus)
• Extensive experience with AWS core services, including S3, EC2, Lambda, RDS, and EMR, and practical experience using Boto3 and AWS machine learning services such as SageMaker and Bedrock
• Strong scripting and programming skills in Python and Bash and proven experience operating Linux-based production systems
• Ability to work onsite in Stamford, CT and to communicate and troubleshoot effectively across cross-functional, distributed teams
• Commitment to the highest ethical standards