Purpose of the Job
We are looking for a Staff AI Platform & Agent Runtime Engineer to build the foundation for enterprise-scale AI and agent execution at EQ Bank. In this role, you will architect and operate the platform that powers our next generation of AI agents from experimentation through production, enabling teams across the organization to build, deploy, and scale intelligent agentic workloads securely and reliably.
You will sit at the intersection of platform engineering, MLOps, and agentic AI, shaping the runtime, tooling, and developer experience that accelerates AI adoption across every business domain. This is a hands-on leadership role for someone who thrives on solving hard infrastructure problems and setting the technical direction for a rapidly evolving space.
Main Activities:
• Define enterprise AI platform architecture and roadmap.
• Design, build and operate AI-native CI/CD platforms.
• Implement secure-by-design AI controls and governance.
• Define reliability, observability, resilience and FinOps practices.
• Lead architecture reviews and developer enablement programs.
• Provide technical leadership across engineering teams.
Knowledge/Skill Requirements:
• 7+ years of software, platform, or cloud engineering experience, with 3+ years in AI/ML platforms or agentic AI systems.
• Deep hands-on expertise with Azure (AKS, networking, private endpoints, identity, Key Vault) and Azure AI Foundry or equivalent AI platforms.
• Proven experience building CI/CD pipelines for ML/LLM workloads (model, prompt, and agent lifecycle management).
• Strong background in distributed systems, container orchestration (Kubernetes), and API/SDK design.
• Experience with agent frameworks (e.g., Semantic Kernel, LangChain, AutoGen) and orchestration patterns (memory, tools, planning).
• Solid understanding of LLM inference optimization, model routing, evaluation, and observability.
• Track record of establishing platform standards, paved paths, and developer enablement at scale.
• Excellent collaboration and communication skills across engineering, security, risk, and business stakeholders.
Preferred Qualifications
• Prior experience in regulated industries (financial services, banking, insurance).
• Familiarity with Microsoft Fabric, Power Platform, Copilot, and Copilot Studio integrations.
• Experience with FinOps for AI workloads including cost attribution, token accounting, and model economics.
• Background in Responsible AI, model governance, and evaluation frameworks.
• Contributions to open-source AI/agent platform projects.
