DevOps Engineer with MLOps

Pay Range: $56.92hr - $60.09hr

Job Overview:

Design, build, and own DevOps platforms and applications that leverage AI/ML to improve developer productivity, reliability, and security.

Responsibilities:

• Architect, implement, and maintain solutions using modern DevOps, MLOps, and AIOps tools and practices, including LLMs, vector search, and AI-assisted automation.
• Develop and enhance AI-powered features for CI/CD, observability, and incident management, including intelligent alerting, anomaly detection, and automated remediation workflows.
• Lead and contribute to the development of automation scripts, tools, and services using Python (preferred) and other languages (TypeScript/JavaScript, Bash, Groovy) to integrate with REST and AI APIs.
• Collaborate with cross-functional teams (software engineering, SRE, security, data, and platform teams) to define requirements, design scalable solutions, and drive adoption of DevOps and AI/ML best practices.
• Implement and optimize CI/CD pipelines using systems such as GitHub Actions, Jenkins, Kubernetes, and Docker to ensure high reliability, security, and velocity of software delivery.
• Use AI-driven observability and chatops to troubleshoot and resolve production issues, improve mean time to detection (MTTD) and mean time to resolution (MTTR), and enhance system resilience.
• Champion and operationalize AI copilots, chatops, and knowledge-automation tools across teams to streamline workflows, reduce manual toil, and improve developer experience.
• Establish and enforce standards for source control, code review, and policy-as-code (e.g., trunk-based development, code ownership, and automated quality/security gates).
• Contribute to technical design reviews, documentation, and continuous improvement of DevOps and AI/ML-enabled platform capabilities.

Preferred Experience:

• Hands-on experience with modern DevOps tooling and practices, including CI/CD platforms such as GitHub Actions and Jenkins, as well as containerization and orchestration with Docker and Kubernetes.
• Practical experience applying AI in DevOps, such as LLM-powered PR summarization, test generation, issue triage, anomaly detection on logs and metrics, or chatops for incident response and operational support.
• Strong scripting and automation skills with proficiency in Python, experience in TypeScript/JavaScript, Bash, or Groovy, and the ability to build small to medium-scale automations and integrate with REST and AI APIs.
• Solid understanding of source control and code review workflows including GitHub, GitLab, Bitbucket, or Gerrit, alongside familiarity with trunk-based development, codeowners, and policy-as-code enforcement.
• Experience with relational databases and SQL, including an understanding of database concepts, schema design, and performance considerations.
• Knowledge of computer hardware components and experience installing, configuring, and troubleshooting hardware and software issues.
• Demonstrated ability to work effectively in a fast-paced, collaborative engineering environment, supported by strong verbal and written communication skills and interpersonal abilities.

Academic Credentials:

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent.