AI Enablement Engineer

Responsibilities

AI Adoption & Stakeholder Enablement

Partner with Department Heads and business stakeholders to identify workflow pain points, assess AI applicability, and design and deliver tailored AI solutions using Google Gemini and Claude Enterprise.

Build and maintain a library of reusable AI skills, prompt templates, and custom instructions for common business functions across departments such as finance, compliance, operations, and customer support.

Plan, deliver, and iterate on AI training sessions, workshops, and onboarding materials for both business and technical audiences; track adoption metrics and report progress to management.

Developer Enablement

Embed with engineering teams to coach developers on effective AI-assisted development practices, including advanced Claude Code usage, prompt patterns, and MCP server integrations that improve engineering productivity.

Design, build, and maintain AI agents and automated workflows that integrate enterprise AI capabilities with internal systems via REST APIs and MCP servers.

Security, Governance & Compliance

Ensure all AI solutions and integrations are designed and deployed in alignment with BTSE’s information security policies, data handling standards, and regulatory obligations; work closely with the Information Security team to review AI use cases for data classification and acceptable use compliance.

Maintain documentation of approved AI tools, integration patterns, and usage guidelines; contribute to AI governance artefacts such as acceptable use policies, risk assessments, and audit trail requirements.

Programme Management & Reporting

Own the AI enablement programme roadmap: prioritise initiatives across departments, manage concurrent workstreams, communicate status to leadership, and measure outcomes against defined adoption and productivity KPIs.

Stay current with the rapidly evolving AI tool landscape; evaluate new capabilities across our enterprise platforms, run proof-of-concept pilots, and make evidence-based recommendations on expansion or new tooling.

Requirements:

3+ years of experience in a technical role such as solutions engineer, developer advocate, technical consultant, or software engineer, with a strong focus on AI/ML tooling or enterprise enablement.

Hands-on experience with Claude Enterprise (claude.ai) and/or the Anthropic API, including working with system prompts, tool use, and multi-turn conversations.

Practical knowledge of Google Gemini for Workspace (Docs, Sheets, Gmail, Meet) and the ability to guide end users in applying it effectively to business tasks.

Ability to design and build reusable AI skills, custom instructions, and prompt templates tailored to specific business functions (e.g. finance reporting, compliance summaries, customer support drafting).

Demonstrated ability to engage with non-technical stakeholders, conduct workflow discovery sessions with Department Heads, and translate business requirements into AI-powered solutions.

Ability to work alongside software engineers, providing guidance on effective use of AI-assisted development tools such as Claude Code, and recommending prompt patterns or agentic workflows that improve engineering productivity.

Programming proficiency in at least one language (Python, Java, Go, or JavaScript) sufficient to build proof-of-concept integrations, automation scripts, and AI agent workflows.

Familiarity with REST APIs and the ability to integrate AI capabilities into existing internal tooling or workflows via API calls.

Experience facilitating training sessions, workshops, or knowledge-sharing sessions on AI tools and best practices for mixed-audience groups (business users and technical staff).

Strong written and verbal communication skills; able to document AI solutions clearly for both end-user guides and technical handover.

Able to effectively listen, speak, read and write in English, with Chinese as a plus.

Nice to haves:

Familiarity with Model Context Protocol (MCP) — either consuming existing MCP servers or building custom ones to extend AI tool access to internal systems.

Exposure to vector databases, embeddings, or RAG pipelines (e.g. pgvector, Chroma, or similar) for grounding AI outputs in company-specific knowledge.

Knowledge of information security principles and data handling requirements in a regulated industry (fintech, crypto, or financial services); understanding of how to apply AI tools within a data governance framework.

Background in change management or organisational adoption programmes; experience measuring and reporting on software or AI adoption KPIs.

Familiarity with the fintech or cryptocurrency domain — understanding of common workflows across operations, compliance, customer support, and engineering teams.

Prior experience authoring internal knowledge bases, wikis (e.g. Confluence), or developer-facing documentation for AI tools and processes.

Perks and Benefits

Competitive total compensation package
Various team building programs and company events
Comprehensive healthcare schemes for employees and dependants
And many more! Apply and let us tell you more!