Forward Deployed Engineer, AI and Agentic SDLC

Career Tools

About This Role

GitLab operates as an intelligent orchestration platform for DevSecOps, helping organizations boost developer efficiency, streamline operations, mitigate compliance risks, and expedite digital transformations. Millions of registered users and over half of the Fortune 100 rely on the company's software. The Forward Deployed Engineering team bridges the divide between customer requirements and standard product offerings. In the role of Staff Forward Deployed Engineer focusing on AI and Agentic SDLC, you will…

Job Description

Responsibilities

• Design and implement agentic systems incorporating models, tools, action loops, orchestration, context, retrieval, state, memory, evaluation, tracing, and human oversight
• Develop knowledge and context systems utilizing retrieval-augmented generation, cache-augmented generation, graph-based retrieval, repository comprehension, and context optimization
• Engage directly within strategic client environments to diagnose technical friction and transform complex requirements into functional solutions
• Build agent workflows, system integrations, evaluation suites, reference architectures, and product features that elevate customer results
• Contribute production-ready code changes to the GitLab Duo Agent Platform and the wider product
• Analyze and enhance system quality, dependability, latency, operational cost, token usage, security, governance, and developer experience
• Troubleshoot issues across models, context, retrieval mechanisms, tool execution, control flow, and surrounding development workflows
• Collaborate closely with product and engineering groups to accelerate delivery while adhering to established standards
• Convert client-specific findings into general platform features, design patterns, and technical guidelines
• Provide high-level technical leadership through architecture, implementation, code review, mentorship, and cross-team influence

Requirements

• Staff-level software engineering background in creating, shipping, and refining complex production systems
• Practical experience in designing, evaluating, tracing, debugging, and optimizing substantive AI architectures
• Strong knowledge of how agentic frameworks utilize models, context, retrieval, tools, state, control flow, and evaluation
• Understanding of the common reasons why artificial intelligence systems experience unreliability, low adoption rates, or excessive resource consumption
• Specialized depth in AI engineering domains such as agent frameworks, context systems, developer tools, inference, or AI-enabled product creation
• Proficiency in programming languages used for backend, platform, developer tooling, or AI systems such as Python, TypeScript, Go, Rust, Java, Kotlin, Ruby, or C#
• Capability to learn and contribute across large, mature, and polyglot codebases
• Practical awareness of artificial intelligence safety, authorization, governance, observability, and cost management
• Clear communication skills for interacting with enterprise customers and senior stakeholders while managing ambiguity and building trust
• Demonstrated history of staff-level impact through technical direction, reusable architectures, and cross-functional leadership

Qualifications

• Experience working directly alongside customers, design partners, or external engineering teams
• Background in building AI systems for software engineering, coding agents, IDEs, code understanding, review, testing, CI/CD, security, or incident response
• Familiarity with LangChain, LangGraph, RAG, cache-augmented generation, knowledge graphs, or agent evaluation tracing
• Experience analyzing how model and inference behaviors impact application performance
• Familiarity with self-hosted models, restricted environments, enterprise governance, or customer-controlled inference
• Familiarity with Git, GitLab, CI/CD pipelines, developer platforms, or large open-source projects
• Exposure to Ruby on Rails or other components of the existing platform stack
• Experience working with cloud environments like AWS, GCP, or Azure, alongside Kubernetes
• Familiarity with Infrastructure as Code tools including Terraform and Ansible
• Evidence of technical leadership such as public writing, architecture guidance, open-source contributions, or speaking engagements

Core Skills

Benefits

• Flexible Paid Time Off
• Team Member Resource Groups
• Equity Compensation and Employee Stock Purchase Plan
• Growth and Development Fund
• Parental Leave

Frequently Asked Questions

Answers are based only on the employer’s listing; where it doesn’t say, neither do we.

Sample Interview Questions

AI-generated questions tailored to this specific role — a preview of the full practice set.