AI Engineer
New
G
GitLabEnterprise AI
Remote, BangaloreFull-Time
Salary not disclosed
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Job Details
- Required Skills
- GraphQLPythonSalesforceRESTful APIsPrompt EngineeringLLM
Requirements
- Build end-to-end solutions independently, write clean and maintainable code, and debug effectively.
- Have strong proficiency in at least one modern scripting language, such as Python or JavaScript/TypeScript.
- Understand REST APIs, GraphQL, and integration patterns.
- Apply prompt engineering, including system prompt design, context-window management, multi-turn interactions, and output evaluation.
- Make model-selection and cost-performance decisions, including when to use fine-tuned models or RAG.
- Understand agentic architecture patterns, including tool use, multi-agent orchestration, human-in-the-loop designs, guardrails, and evaluation frameworks.
- Have practical familiarity with models from Anthropic, OpenAI, and open-source alternatives.
- Design AI safety measures such as input validation, output filtering, access controls, prompt-injection defenses, and data-leakage prevention.
- Map complex processes, identify bottlenecks, and trace problems to root causes.
- Understand enterprise data models and workflows and be familiar with business systems such as Salesforce, Marketo, Zendesk, Workato, Relevance AI, or Glean.
- Own complex initiatives from discovery through delivery and drive measurable outcomes independently.
- Scope MVPs, prioritize work, deliver iteratively, and consider adoption, user experience, and business outcomes.
- Preferred: experience with GitLab and CI/CD, consulting or solutions engineering, value stream mapping or flow metrics, orchestration tools, startups, or mentoring junior engineers.
Responsibilities
- Map business workflows, identify constraints, and determine whether AI is an appropriate solution.
- Own AI initiatives from stakeholder discovery and technical design through implementation, deployment, and iteration.
- Design, develop, and ship AI-powered solutions and working prototypes.
- Build solutions that reduce bottlenecks, shorten lead times, and increase throughput.
- Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI platforms.
- Use GitLab AI offerings where appropriate and share real-world usage insights with R&D.
- Partner with stakeholders across functions to understand constraints and align on outcomes.
- Track success through business metrics, flow metrics, adoption, ROI, and feedback.
- Evaluate tools, document patterns, and create reusable technical foundations.
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