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Looking for Developer/Trainer - AI Enablement Lead

Location: Plano, Texas (4 Days Onsite)

Description

Position Summary:                                                                                                                 

We are seeking a highly motivated and experienced Developer AI Enablement Lead with 5 years of experience to join our dynamic Business Support team. The ideal candidate with a strong background in software engineering, solution architecture, DevOps, developer enablement, or technical training, who has hands-on experience using AI tools in software development workflows. The ideal candidate combines technical credibility with excellent communication, facilitation, and learning design skills, and can effectively engage engineers, architects, product teams, and technology leaders to drive enterprise AI adoption.

The successful candidate will be responsible for designing and delivering hands-on AI training programs, workshops, labs, demos, and enablement materials for technical teams; promoting responsible and effective use of AI across the software development lifecycle; creating reusable learning assets and technical playbooks; facilitating technical events and hackathons; collaborating with cross-functional stakeholders; and helping engineering teams adopt AI tools and practices in a practical, secure, and measurable way.                                                                                                            

Essential Functions:                                                                                                              

• Design and deliver hands-on AI training for software engineers, developers, architects, technical product teams, and related technology audiences.
• Build developer-focused curriculum, workshops, labs, demos, facilitator guides, job aids, and reusable learning assets.
• Teach practical use of AI across the software development lifecycle, including requirements analysis, code generation, debugging, refactoring, documentation, test creation, code review, release support, and technical problem solving.
• Create technical examples that are realistic, credible, and useful for engineering teams.
• Facilitate live technical workshops, virtual sessions, bootcamps, lunch-and-learns, hackathon-style events, and internal enablement sessions.
• Partner with engineering, architecture, cybersecurity, data, cloud, product, and responsible AI stakeholders to ensure training reflects approved tools, standards, and enterprise expectations.
• Help teams understand when AI is useful, when it is risky, and when human review is required.
• Support the development of prompt libraries, technical playbooks, lab exercises, reference examples, and reusable patterns for technical users.
• Translate complex AI concepts into practical guidance for technical audiences without oversimplifying important risks or limitations.
• Gather learner feedback, technical questions, use cases, and adoption barriers to improve future enablement.
• Help identify common engineering use cases that may require additional documentation, governance review, technical support, or escalation.
• Stay current on emerging AI development tools, coding assistants, agentic workflows, model capabilities, and enterprise AI practices.
• Advise on smart implementation that aligns the right models to the right job and reflects in transparent token consumption and cost management

Requirements

Minimum qualification:                                                                                                                      

Required Education & Experience:

• Bachelor’s degree or equivalent experience
• 5 years of Experience in software engineering, solution architecture, DevOps, platform engineering, technical product delivery, developer relations, or technical enablement.
• Hands-on experience using AI tools in technical workflows, such as AI-assisted coding, debugging, documentation, testing, research, or automation.
• Ability to design and facilitate technical training for engineering audiences.
• Strong understanding of software development lifecycle practices, including requirements, development, testing, code review, deployment, documentation, and operational support.
• Ability to explain technical concepts clearly to mixed audiences, including engineers, managers, and non-technical stakeholders.
• Strong communication, facilitation, and presentation skills.
• Comfort running live demos and adapting when tools, environments, or participant questions do not go as planned.
• Ability to build practical exercises, examples, and learning assets that participants can apply immediately.
• Awareness of responsible AI, security, privacy, intellectual property, and human-in-the-loop review considerations.
• Strong collaboration skills in a large enterprise environment.

Preferred Qualifications

• Experience with AI coding assistants such as GitHub Copilot Coding Assistant, OpenAI Codex, Claude Code, AWS Kiro, or similar tools.
• Experience with prompt engineering for technical workflows.
• Familiarity with RAG, agents, LLM evaluation, orchestration patterns, APIs, cloud platforms, or enterprise AI architectures.
• Experience creating technical labs, sample repositories, code walkthroughs, enablement guides, or developer documentation.
• Experience supporting hackathons, developer communities, technical bootcamps, or internal technology events.
• Experience with secure coding, application security, cloud security, DevSecOps, or governance-heavy enterprise environments.
• Experience working with product teams, agile delivery teams, engineering leaders, or architecture review groups.
• Prior experience in developer advocacy, technical training, technical program management, or engineering enablement.

What Success Looks Like

Success in this role means technical teams are not just aware of AI tools — they are using them more effectively, responsibly, and consistently.

The role will help drive:

• Increased confidence and adoption of approved AI tools among technical teams.
• Higher-quality developer enablement materials, labs, and technical examples.
• More practical, hands-on learning experiences for engineers.
• Reusable technical playbooks and prompt patterns.
• Better understanding of where AI fits into engineering workflows.
• Clearer guidance on responsible and secure AI-assisted development.
• More consistent capture of technical use cases, questions, and adoption barriers.
• Stronger alignment between AI enablement, engineering practices, and enterprise technology standards.

Ideal Candidate Profile

You may be a strong fit if you are the kind of person who can:

• Sit with software engineers and earn credibility quickly.
• Explain AI without hype.
• Teach through demos, examples, and hands-on practice rather than long slide decks.
• Build content from scratch when the topic is new or still evolving.
• Turn complex technical topics into clear learning paths.
• Facilitate skeptical or advanced technical audiences.
• Balance innovation with enterprise guardrails.
• Help teams move from curiosity to practical adoption.