Distinguished Engineer, Agentic SDLC & Non‑Linear Productivity

Gitlab

RemoteFull-timebased eligibility requirements. Our Talent Acquisition team can help answer anyai

The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating

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Skills in this role

AI fundamentals
Experimentation

Courses relevant to this posting

Relevance is based on skills and concepts — not a guarantee that you qualify.

Problem Framing and Verification for AI-Assisted Workflows
Covered skills:AI fundamentalsExperimentation
Skills to close:None listed

Build the skills for this job

Coming-soon courses matched to this posting — outline, waitlist, and skill path.

Course DNA for this role

Problem Framing and Verification for AI-Assisted Workflows

7 chapters · 31 lessons

1. Problem Decomposition for AI Systems
4 lessons
Learn to break down ambiguous requests into well-defined tasks that AI can handle reliably.
2. Context Engineering for Better Outputs
4 lessons
Provide the right background, examples, and constraints to improve AI output quality.
3. Verification Strategies for Generated Code
5 lessons
Build systematic checks to catch errors, security issues, and maintainability problems in AI-generated code.
4. Validating Requirements and Specifications
4 lessons
Assess AI-generated product requirements, tickets, and specs for completeness and feasibility.
5. Designing Human-in-the-Loop Workflows
5 lessons
Decide where AI fits in your process and where human review is required.
6. Evaluating AI System Limitations
4 lessons
Understand when AI outputs are unreliable and how to recognize failure modes.
7. Building Accountability into AI-Assisted Work
5 lessons
Maintain professional responsibility and quality standards when using AI tools.

Want the full course when it launches? Join the waitlist and we will notify you.

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Skills in this role

Courses relevant to this posting

Build the skills for this job

Problem Framing and Verification for AI-Assisted Workflows

1. Problem Decomposition for AI Systems

2. Context Engineering for Better Outputs

3. Verification Strategies for Generated Code

4. Validating Requirements and Specifications

5. Designing Human-in-the-Loop Workflows

6. Evaluating AI System Limitations

7. Building Accountability into AI-Assisted Work

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