AI Enablement Engineer

1w1 week ago

AgileEngine

Florianópolis, BR · Full-time · BRL 180,000 – BRL 300,000

About this role

AgileEngine is an Inc. 5000 company creating award-winning software for Fortune 500 brands and startups across 17+ industries. We lead in application development and AI/ML with a people-first culture earning multiple Best Place to Work awards. As AI Enablement Engineer, sit at the center of engineering AI integration spanning tooling, agentic SDLC platform, and product incubation.

Work closely with AI Enablement Lead, Director of Engineering, and product leadership to translate internal AI workflows into customer features. Own day-to-day configuration and optimization of AI coding tools. Act as primary contact triaging issues like integration failures or model compatibility for engineers.

Maintain onboarding docs, runbooks, enablement sessions, and office hours to drive AI adoption across teams. Gather feedback to refine workflows and expand internal agentic pipeline connecting specification to validation. Own AI agents' integration with development toolchain including third-party tools and context strategies.

Prototype AI-assisted experiences for SaaS customers and serve as internal test bed for features. Design experiments validating agent configs and evaluate emerging tools for integration. Maintain dogfood culture using products internally to inform development with real insights.

Requirements

5+ years of experience in software engineering, DevOps, or platform engineering role with backend language such as PHP, Java, TypeScript, JavaScript, Python, C#, .NET, or Golang
1+ years of hands-on experience with LLM-based tools or agentic workflows in a professional setting
Hands-on experience with AI coding assistants such as Claude Code, Windsurf, Amp, or Cursor
Direct experience deploying and supporting AI coding assistants across a large engineering team including configuration, rollout, and troubleshooting
Comfort working across multiple technologies and a mixed-technical environment
Ability to understand complex engineering problems and design practical, scalable solutions
Familiarity with LLM infrastructure concepts including model routing, API proxying, token management, and model versioning
Strong written communication skills

Responsibilities

Own day-to-day configuration and optimization of AI coding and productivity tools
Act as primary point of contact for engineers, triaging and resolving issues like integration failures, model compatibility, or configuration drift
Maintain onboarding documentation and runbooks for effective AI use by all engineers
Run enablement sessions and office hours to drive adoption and best practices
Gather feedback from teams to translate usage patterns into workflow improvements
Drive refinement and expansion of internal agentic pipeline for specification, implementation, testing, and validation
Own integration layer between AI agents and development toolchain including third-party integrations
Prototype AI-assisted developer experiences that graduate into product features