Senior Software / Site Reliability Lead Engineer

Role Description

What You Will Own:

• Cross-pod reliability standards: Set the reliability bar and ensure it is met consistently across applications. Collaborate with Functional SREs to connect technical reliability metrics to business-side outcomes. You own the engineering signal; together you tell the full reliability story.
• SLOs and reliability metrics: Own definitions of service level objectives for every AI service that goes to production. Establish error budgets and use them to drive engineering decisions — not just measure uptime.
• Monitoring and observability: Implement and maintain the full observability stack — logging, metrics, tracing, and dashboards. You will know when something is degrading before users do. Design and manage alerting infrastructure that tells you what's wrong, not just that something is wrong. Alerts you build catch real problems; they don't cry wolf.
• Incident response: Own on-call procedures, escalation paths, and incident management end-to-end. Lead post-incident reviews and maintain the reliability improvement backlog. When something breaks, you coordinate the response and ensure it doesn't break the same way again.
• Production Readiness: Define and enforce the criteria that determine whether an AI service is ready for production. You are the gate between "it works in dev" and "it's ready to ship."
• Toil elimination: Identify and automate repetitive operational tasks. If a human is doing something a script could do, you fix that.

What You Won't Own:

• Infrastructure provisioning — IT provides the infrastructure; you define what's needed and validate it works.
• Business process decisions or backlog prioritization.
• Business-side reliability metrics - you partner with the Functional SRE on those, but they own that domain.

What Makes This Role Different:

• AI services have failure modes that traditional applications don't — model drift, token budget exhaustion, prompt injection, upstream data quality degradation. You will build monitoring for problems that most SRE teams have never encountered.
• You are applying SRE principles from scratch. There is no existing SRE practice to inherit — you will define it for the platform.
• Your production readiness criteria directly determine whether AI services go live. You have real authority to say "not ready."
• You operate across projects simultaneously — embedded deeply enough to understand large-scale systems, while maintaining consistent standards across all projects.
• Your software engineering background means you can engage directly with development teams at the design level — catching reliability problems before they become operational ones.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related field, plus 8 years of experience; or Master’s degree plus 6 years of experience.
• Production SRE or DevOps experience — you have owned the reliability of systems that real users depended on, not just built CI/CD pipelines.
• Hands-on experience with monitoring and observability tools — Prometheus, Grafana, Datadog, ELK, CloudWatch, or similar. You have built dashboards and alerts that caught real problems.
• Strong scripting and automation skills — Python, Bash, infrastructure-as-code (Terraform, CloudFormation, or similar).
• Experience with containerized environments — Docker, Kubernetes, container orchestration at scale.
• Experience defining and managing SLOs, error budgets, and incident response procedures in production.
• U.S. citizenship required. Department of Defense Secret security clearance is required at time of hire.

Requirements

• Production SRE or DevOps experience — you have owned the reliability of systems that real users depended on, not just built CI/CD pipelines.
• Software engineering fundamentals — you can read, write, and meaningfully review production-quality code. You understand how architectural and design decisions made early translate into operational problems later.
• Software design experience — you have participated in or led design reviews, defined service interfaces or APIs, and pushed back on design decisions using reliability and operability as criteria.
• Hands-on experience with monitoring and observability tools — Prometheus, Grafana, Datadog, ELK, CloudWatch, or similar. You have built dashboards and alerts that have caught real problems.
• Strong scripting and automation skills — Python, Bash, infrastructure-as-code (Terraform, CloudFormation, or similar).
• Experience with containerized environments — Docker, Kubernetes, container orchestration at scale.
• Experience defining and managing SLOs, error budgets, and incident response procedures in production.

Benefits

• Remote — 100% telework.
• 9/80 schedule.
• Defense industry experience is not required.

Company Description

General Dynamics Mission Systems (GDMS) engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. With a global team of 12,000+ top professionals, we partner with the best in industry to expand the bounds of innovation in the defense and scientific arenas. Given the nature of our work and who we are, we value trust, honesty, alignment and transparency. We offer highly competitive benefits and pride ourselves in being a great place to work with a shared sense of purpose. You will also enjoy a flexible work environment where contributions are recognized and rewarded. If who we are and what we do resonates with you, we invite you to join our high-performance team!