Senior AI Enablement Engineer at MUFG FUND SERVICES UK LIMITED

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

22 Sep, 26

Salary

0.0

Posted On

24 Jun, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

TypeScript, JavaScript, Vue.js 3, AI Tooling, Software Architecture, CI/CD, Automated Testing, API Integration, Node.js, Python, AWS, AI Governance, RAG, Developer Experience, Observability, Technical Documentation

Industry

Financial Services

Description

Company Description MUFG Investor Services is a trusted partner to many of the world’s largest public and private funds, providing asset servicing and operational solutions built for alternatives. With over $1 trillion in client assets under administration, we offer fund administration, banking, payments, fund financing, foreign exchange overlay, corporate and regulatory services, custody, business consulting, and more. Operating from 17 locations worldwide, we help clients mitigate risk, enhance efficiency, and navigate the operational complexities of today’s investment management landscape. As a division of Mitsubishi UFJ Financial Group (MUFG), one of the world’s largest financial institutions with approximately $3 trillion in assets, we combine deep expertise with the strength and stability of a leading financial institution. To learn more, visit us at www.mufg-investorservices.com. Job Description 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. Additional Information What’s in it for you to join MUFG Investor Services? Take a look at our careers site and you’ll find everything you’d expect from a career with the fastest-growing business at one of the world’s largest financial groups. Now take another look. Because it’s how we defy expectations that really defines us. You’ll feel that difference in all kinds of ways. Our vibrant CULTURE. Connected team. Love of innovation, laser client focus, and next-level LEARNING & DEVELOPMENT. So, why settle for the ordinary? Apply now for a Brilliantly Different career. We thank all candidates for applying; however, only those proceeding to the interview stage will be contacted. MUFG is an equal opportunity employer. Rank: Associate Director Employment Type: Permanent Full Time

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Responsibilities

Design and implement internal tools, standards, and workflows to enable the safe and scalable adoption of AI-assisted development across engineering teams. Partner with security, architecture, and platform teams to integrate AI capabilities into the enterprise software delivery lifecycle.