The Role

Location

This is a work from home position within the US.

The Main Responsibilities

• Design and implement AI-enabled developer workflows for code generation, reviews, debugging, testing, documentation, research, incident triage, and platform navigation. Establish scalable, secure, and repeatable patterns for AI adoption across engineering teams.
• Develop reusable prompts, agents, integrations, and workflow accelerators that improve developer productivity while aligning with architecture, security, governance, and operational standards.

• Identify development friction and translate it into high-impact AI solutions. Partner with engineering teams to reduce toil, accelerate delivery, improve onboarding, and enhance the developer experience from ideation through production.
• Define adoption metrics, feedback loops, and success measures to demonstrate improvements in quality, delivery speed, developer confidence, and overall performance.

• Build and scale shared AI capabilities, reference architectures, prompt libraries, templates, and playbooks that teams can adopt quickly and consistently.
• Partner with platform, architecture, and security teams to ensure AI solutions are maintainable, discoverable, observable, governed, and aligned with responsible AI practices.

• Improve access to engineering knowledge through AI-powered search, summarization, technical Q&A, documentation generation, and context-aware support.
• Transform tribal knowledge, coding standards, platform guidance, and operational playbooks into scalable AI-assisted experiences.

• Drive AI adoption through demos, training, office hours, onboarding resources, reusable examples, and engineering community engagement.
• Coach developers and technical leaders on practical AI usage, safe experimentation, measurable outcomes, and responsible engineering practices.

• Lead AI enablement initiatives from discovery through production. Define plans, milestones, dependencies, user validation, and production readiness.
• Identify and mitigate risks related to security, data access, hallucinations, quality, compliance, integrations, user experience, and operational support.

• Measure AI impact through adoption, usage, cycle time reduction, automation, developer satisfaction, and quality improvements.
• Use data and feedback to refine AI enablement strategies, retire low-value initiatives, and scale high-impact capabilities.

• Provide technical leadership and mentorship for AI developer enablement. Foster innovation, accountability, collaboration, experimentation, and continuous improvement.
• Guide teams on architecture, prompt engineering, agent workflows, integration patterns, responsible AI, and safe implementation of AI-powered developer solutions.

What We Look For in a Candidate

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field. Advanced degree or equivalent practical experience preferred.
• 10+ years of experience in software development, platform engineering, developer productivity, AI enablement, automation, or related technical disciplines.
• Strong hands-on experience designing, building, and supporting modern software solutions, developer tools, APIs, integrations, automation frameworks, or internal platforms.
• Experience with AI-assisted development workflows, coding assistants, generative AI capabilities, prompt engineering, agentic workflows, or applied AI solutions for engineering teams.
• Deep understanding of the software development lifecycle, code quality, testing, documentation, secure coding, CI/CD, operational reliability, and production support practices.
• Proven ability to translate ambiguous developer needs into practical, secure, scalable, and reusable technical solutions.
• Experience partnering with cross-functional teams including engineering, platform, security, product, architecture, operations, and business stakeholders
• Strong communication skills with the ability to explain technical concepts, demonstrate AI-enabled workflows, influence adoption, and create clear enablement materials.
• Strong understanding of responsible AI, data protection, security considerations, governance, and risk management for enterprise AI adoption.
• Demonstrated ability to mentor engineers, lead technical initiatives, and drive measurable improvements in developer productivity and engineering outcomes.

Preferred Skills

• Experience with LLM-based developer tools, AI coding assistants, agent frameworks, retrieval-augmented generation, vector search, tool calling, or workflow orchestration.