Lead AI Enablement Engineer – Software Delivery Lifecycle & Developer Platform
Indexed description
We’re unique too. We were established to find a better way for the global financial community to move value – a reliable, safe and secure approach that the community can trust, completely. We’re always striving to be better and are constantly evolving in an ever-changing landscape, without undermining that trust. Five decades on, our vibrant community reflects the complexity and diversity of the financial ecosystem. We innovate diligently, test exhaustively, then implement fast. In a connected and exciting era, our mission has never been more relevant. Swift now has a presence in 200+ countries and legal territories to serve a community of more than 12,000 banks and financial institutions.
Key Responsibilities
Solution Architecture and Design
- Collaborate with Enterprise Architecture and Security teams to define the AI architecture supporting the software development lifecycle, aligned with enterprise standards and governance.
- Design scalable, secure, and reusable AI services that integrate seamlessly with our developer platform.
- Evaluate and integrate AI solutions that improve developer productivity across planning, coding, testing, security, release management, and operations.
- Build production-grade integrations and proof-of-concepts using modern AI frameworks and APIs.
- Develop Agentic AI workflows to automate engineering activities while maintaining transparency, auditability, and human oversight.
- Design and implement MCP (Model Context Protocol) integrations enabling AI assistants to securely interact with enterprise tools and development platforms.
- Develop reusable services, SDKs, templates, and reference implementations that accelerate AI adoption.
- Contribute code and participate in architecture reviews and technical design discussions.
- Embed AI into the end-to-end software delivery pipeline, source control, testing, security scanning, documentation, and release processes.
- Drive automation opportunities using AI while respecting software quality and compliance requirements.
- Continuously evaluate emerging AI capabilities relevant to software engineering.
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