DevOps Engineer II/ Senior

Rocket Lab Corporation•Littleton, CO

6d•$115,000 - $170,000•Onsite

About The Position

As a DevOps Engineer II/ Senior based at Rocket Lab's site in Littleton, CO, you will revolutionize software quality assurance through AI-driven automation and intelligent systems. This role sits within the DevSecOps organization and focuses on building autonomous AI ecosystems that continuously monitor, analyze, and improve software quality across all organizational programs and platforms. You will design and implement AI agents that automatically troubleshoot CI/CD testing failures, generate actionable insights, identify quality gaps, and proactively address findings within organizational software before they impact mission-critical operations. This is a highly technical, automation-focused role for someone who sees software quality engineering as an opportunity to build intelligent systems that work 24/7. You will create AI-powered reporting dashboards, automated notification systems, and predictive analytics that give leadership real-time visibility into software health across the entire organization. Your work will directly close quality gaps and elevate the security, reliability, and performance of software systems supporting space missions.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or other technical discipline
2+ years of experience in professional software development including software verification and validation practices (for DevOps Engineer II)
5+ years of experience in professional software development including software verification and validation practices (for Senior DevOps Engineer I)
Experience in AI/ML technologies with hands-on experience building AI-driven automation systems
Demonstrated experience using AI tools, LLMs (ChatGPT, Claude, etc.), and AI coding assistants in daily workflows
2+ years of proficiency in Python skills with experience in AI/ML libraries and frameworks (for DevOps Engineer II)
5+ years of proficiency in Python skills with experience in AI/ML libraries and frameworks (for Senior DevOps Engineer I)
Knowledge of CI/CD pipeline automation tools and DevSecOps practices
Experience with data analysis, metrics collection, and building reporting/dashboard systems
Knowledge of the Software Development Life Cycle and V-Model
U.S. citizenship is required, due to program requirements

Nice To Haves

Active Secret or Top Secret Clearance
Experience with AI agent frameworks, autonomous systems, or multi-agent orchestration
Proven track record building intelligent automation solutions that solve complex software quality engineering problems
Background in machine learning, natural language processing, or predictive analytics
Proficiency in C and C++ languages
Strong problem-solving skills with an automation-first mindset
Proven track record producing or testing high-quality software in CI/CD pipelines
Experience building dashboards using tools like Grafana, Kibana, or custom visualization frameworks
Knowledge of security testing tools and DevSecOps security practices
Aerospace background supporting satellite, spacecraft, constellation, or launch vehicle ground/mission operations
Demonstrated leadership in testing design and working with engineering teams
Ability to produce clear, well-structured, and well-documented designs
Expertise in Git-based workflows, CI/CD pipelines, and Kubernetes orchestration
Experience with observability and monitoring tools (Prometheus, ELK stack, etc.)

Responsibilities

Build and maintain AI-powered quality automation ecosystems that monitor CI/CD pipelines across multiple programs and platforms
Design and implement AI agents that automatically detect, analyze, and troubleshoot software testing failures with minimal human intervention
Develop intelligent reporting systems that provide real-time quality metrics, trend analysis, and actionable recommendations to leadership
Create automated notification and alerting systems that proactively engage teams when quality issues are detected
Identify and close quality gaps in organizational software through systematic analysis of findings, patterns, and root causes
Leverage machine learning and AI tools to predict potential quality issues before they occur
Integrate LLMs and AI assistants into testing workflows to enhance test coverage and defect detection
Produce comprehensive automated test suites that run continuously in CI/CD pipelines
Collaborate with DevSecOps teams to ensure security, quality, and compliance throughout the SDLC
Drive continuous improvement through data-driven insights and AI-enhanced quality processes
Manually test complex edge cases that AI systems flag as high-priority or difficult to automate
Work with development teams through code reviews and integrated pipelines to ensure quality is built in from the start