Senior Staff AI Engineer, Architect
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Job Description
AI Architect responsible for design and evolution of AI systems at TELUS Digital. Integrating Generative AI into scalable and production-ready solutions.
Responsibilities:
- Integrate Generative AI models, such as LLMs, with external APIs, tools, and databases using secure and efficient orchestration patterns.
- Design, develop, and deploy AI workflows and Agentic AI solutions, enabling the seamless orchestration of intelligent agents to plan and perform tasks while leveraging autonomous and/or human-in-the-loop paradigms.
- Implement and optimize multi-agent systems, leveraging standards and protocols such as Model Context Protocol (MCP), and emerging frameworks for agent interoperability and access to external resources.
- Develop evaluation frameworks, metrics, and checkpoints for agent autonomy, performance, and safety, ensuring compliance with moderation, security, and ethical standards.
- Evaluate, analyse, and gather insights out of structured and non-structure data leveraging Generative AI models and pipelines.
- Ensure robust AI agent operations by applying observability, monitoring, and MLOps best practices, facilitating reliable deployment pipelines and continuous performance optimization.
- Orchestrate AI model selection, tuning, and performance validation to meet specific agent-based application needs.
- Communicate complex AI concepts, systems, and decisions effectively to technical and non-technical stakeholders, promoting transparency and trust in AI delivery.
- Foster an environment of innovation and collaboration, engaging and encouraging teams to solve complex problems and share ideas that drive innovative approaches.
Requirements:
- Proven experience designing and scaling complex AI systems, with strong expertise in Generative AI, LLM architectures, and distributed systems.
- Strong background in software and platform architecture, with experience designing API-driven, modular, and extensible systems.
- Deep understanding of orchestration patterns for LLMs, including tool-calling, RAG pipelines, memory systems, and agent coordination frameworks.
- Experience architecting or implementing agentic AI systems using frameworks such as LangGraph, OpenAI Agents SDK, CrewAI, Google ADK, or similar.
- Strong knowledge of data architecture for AI, including embeddings, vector databases, chunking strategies, and retrieval optimization.
- Experience with evaluation frameworks and observability tools (e.g., RAGAS, OpenAI Eval, Arize, LangSmith, Braintrust), and defining system-level metrics.
- Hands-on experience with cloud platforms (preferably GCP) and AI services (e.g., Vertex AI), with a focus on scalable and secure deployments.
- Experience designing systems with CI/CD pipelines, monitoring, logging, and performance optimization at scale.
- Strong understanding of security, governance, and responsible AI practices in production environments.
- Ability to translate complex AI architectures into clear technical and business narratives for diverse stakeholders.
Benefits:
- Health and dental plan
- Life insurance
- Monthly voucher for meals, culture, education, health and mobility
- Child care assistance and more!
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