Distinguished Engineer, Agentic SDLC & Non-Linear Productivity
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GitLabDevSecOps software
Listing locations: Canada, Poland, UK, USAFull-TimePrincipal
Salary250,000 - 349,000 USD per year
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Job Details
- Experience
- 10+ years of software engineering experience, including 4+ years in a Staff, Principal, or equivalent senior technical leadership role
- Required Skills
- CI/CDLLMDistributed Systems
Requirements
- Have 10+ years of software engineering experience.
- Have 4+ years in a Staff, Principal, or equivalent senior technical leadership role.
- Bring deep expertise in AI and ML systems, including large language models, agentic frameworks, and autonomous workflow design at production scale.
- Have led hands-on technical experimentation, defined evaluation frameworks, run benchmarks, and translated findings into scalable architecture decisions.
- Have a strong background in scalable, multi-tenant distributed systems, including service decomposition, fault tolerance, observability, and operational resilience.
- Have experience designing and implementing human-in-the-loop controls, safety guardrails, and responsible AI practices for production systems.
- Demonstrate cross-functional alignment across Engineering, Product, Infrastructure, and Data and ML teams.
- Have experience mentoring senior engineers and influencing technical direction across multiple teams or divisions.
- Be able to work effectively in a fully remote, globally distributed organization with excellent written and asynchronous communication skills.
- Familiarity with GitLab's DevSecOps platform, CI/CD primitives, and the software development lifecycle is a strong plus.
Responsibilities
- Define and refine a company-wide technical vision for autonomous, agentic SDLC.
- Identify and prioritize productivity opportunities across the software development lifecycle.
- Translate ambiguous problem spaces into roadmaps with Product, AI/ML, and Architecture teams.
- Lead hands-on experiments and prototypes for agentic engineering workflows.
- Design reference architectures with orchestration patterns, safety guardrails, observability, and human-in-the-loop controls.
- Define evaluation frameworks and measure correctness, latency, safety, cost, and productivity impact.
- Drive selected internal use cases from concept through adoption and measurable productivity gains.
- Convert proven internal patterns into product capabilities with Product Management and Engineering leadership.
- Mentor Principal and Staff Engineers and guide complex technical and architectural decisions.
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