Description:

TELUS Digital is hiring a forward-thinking AI Engineer for its Enterprise Technology division. This role is uniquely focused on the practical, production-level deployment of Agentic AI and LLM workflows to automate internal software engineering lifecycles. Rather than training deep learning models from scratch, your core mission is to design, configure, and scale context-aware AI agents (utilizing universal patterns like system prompts, tool call routing, and multi-step orchestration) to generate, optimize, and maintain automated test suites across Java/Spring Boot microservices networks. You will serve as an AI enablement champion, designing vendor-agnostic workflows that can seamlessly migrate from tools like Cursor or Claude Code to future architectures, while carefully balancing token budget economics against autonomous execution quality.

Key Technical Skills:

AI Agent Architecture, Multi-Agent Systems, Context Windows & Governance, Tool Use/Function Calling, Prompt Engineering, Agnostic LLM Orchestration, Java/Spring Boot Code Comprehension, Microservices Testing, CI/CD Workflow Automation, Token Budget Management, Cursor/Copilot/Claude Code Tooling

Requirements:

• Proven hands-on track record configuring and deploying AI agents or automated virtual assistants to execute complex, non-deterministic, multi-step code-generation tasks.
• A crystal-clear mental blueprint of universal AI agent patterns, including managing persistent instructions, runtime retrieval strategies, context injection boundaries, and cross-agent communication trees.
• Demonstrated ability to build automation pipelines focused on portability—distinguishing vendor lock-in from absolute standards so that 90%+ of workflows easily transfer across underlying model layers (e.g., transitioning from Kiro to Claude Code or open-source equivalents).
• Solid technical foundation enabling you to parse, comprehend, and map test coverage requirements across high-concurrency Java/Spring Boot microservices, despite not needing to be an expert-level Java programmer.
• Ability to champion, teach, and systematically roll out agent-driven workflows across up to 9 distinct engineering teams possessing highly diverse codebases and varying levels of comfort with AI tools.
• Strong operational understanding of optimization tradeoffs, recognizing when to let agents run autonomously versus when to apply guardrails, alongside tracking token/credit usage metrics.
• A definitive bias toward shipping working software first, subsequently refining and hardening automation logic based on live team feedback.