Senior AI Enablement Engineer

We are hiring a Senior AI Enablement Engineer to help shape how software engineering teams adopt, use, and scale AI-assisted development across the organization. This is not a traditional full‑stack role, and it is not a “prompt engineering” role. You will work at the intersection of software engineering, developer experience, AI tooling, platform standards, governance, and engineering enablement.

You will help build the paved roads that allow engineers to use AI tools safely and effectively: standards, reusable workflows, automation, evaluation approaches, observability, documentation, and internal platform capabilities. The goal is to make AI‑assisted engineering reliable, measurable, secure, and aligned with enterprise software delivery standards.

You will work closely with engineering teams, platform teams, security, architecture, product, and AI stakeholders to turn AI adoption from isolated experimentation into a sustainable engineering capability.

You Will:

• Own and deliver AI enablement capabilities that improve how engineering teams use AI tools across the software development lifecycle.
• Design and build internal tools, workflows, templates, integrations, and automation that make AI-assisted development safer, faster, and more consistent.
• Create and maintain engineering standards, guidance, and reusable patterns for AI-assisted software delivery.
• Help teams adopt AI development tools responsibly, with clear expectations around verification, quality, security, and maintainability.
• Work with engineers/stakeholders to understand real delivery pain points and translate them into practical enablement solutions.
• Build or integrate capabilities around AI-assisted coding, documentation, testing, code review, onboarding, and knowledge retrieval.
• Contribute to AI governance implementation by helping translate policy and security expectations into usable engineering workflows.
• Support observability and measurement for AI adoption, including usage insights, effectiveness, quality signals, and operational risks.
• Partner with platform, architecture, AppSec, and infrastructure teams to ensure AI tools are integrated safely into the enterprise environment.
• Coach and support engineers through workshops, documentation, demos, and hands‑on enablement.
• Operate with high autonomy in a fast‑moving area where ambiguity is expected and practical ownership is essential.

AI & Engineering Enablement Mindset:

• Strong interest in how AI is changing software engineering, developer workflows, and engineering productivity.
• Practical experience using AI development tools such as Claude Code, GitHub Copilot, ChatGPT, Cursor, or similar tools.
• Ability to distinguish between AI experimentation and production‑grade engineering practices.
• Strong belief that AI output must be verified, tested, reviewed, secured, and aligned with engineering standards.
• Comfortable working across technical implementation, team enablement, stakeholder communication, and operational rollout.
• Curious about areas such as AI agents, MCP, RAG, evals, prompt/workflow design, AI observability, and developer productivity measurement.

The Impact You Will Have:

• Help engineering teams adopt AI in a way that is practical, safe, and aligned with enterprise standards.
• Reduce inconsistent AI usage by creating clear workflows, reusable patterns, and trusted guidance.
• Improve developer experience by removing friction from common engineering tasks.
• Help establish measurable AI adoption practices across software engineering.
• Contribute to the next generation of AI‑enabled engineering capability within MUFG Investor Services.

Qualifications You Have (Required):

• 8+ years of professional software engineering experience, with strong ownership of complex systems, platforms, or engineering capabilities.
• Strong proficiency in TypeScript and modern JavaScript. Experience with modern frontend frameworks, preferably Vue.js 3, and scalable frontend architecture patterns.
• Experience working across frontend and backend boundaries, including APIs, integrations, services, and data flows.
• Strong engineering fundamentals: clean architecture, maintainability, automated testing, code reviews, CI/CD, and production readiness.
• Experience designing reusable technical standards, shared libraries, templates, platform tooling, or developer experience improvements.
• Ability to work with multiple engineering teams, understand their delivery challenges, and create practical solutions that scale.
• Strong written communication skills, with the ability to create clear technical guidance, onboarding material, and adoption documentation.
• Ability to operate in ambiguity, structure unclear problems, and move from discovery to delivery.
• Security‑conscious engineering mindset, especially when introducing new tools, automation, or AI‑assisted workflows.

Preferred:

• Experience with AI‑assisted development tools in real software delivery, not only experimentation.
• Experience building internal platforms, developer tooling, engineering productivity tools, or paved‑road capabilities.
• Experience with Node.js, Python, or another backend language.
• Experience with cloud‑based systems, preferably AWS. Experience with observability concepts such as logs, metrics, traces, usage telemetry, and operational dashboards.
• Experience in regulated or enterprise environments with strong governance, security, and release discipline.
• Experience with design systems, frontend platform standards, accessibility, and reusable UI patterns.
• Experience with knowledge retrieval, documentation systems, RAG‑style workflows, or internal engineering knowledge platforms.
• Experience designing lightweight evaluation approaches for AI‑generated outputs, automation, or agentic workflows.
• Experience coaching engineers, running workshops, or driving adoption of new engineering practices.