Engineering Manager, Agentic Ai & Machine Learning

Come make your difference in communities across Canada, where authenticity, trust and making connections is valued - as we shape the future of Canadian retail, together. Our unique position as one of the country's largest employers, coupled with our commitment to positively impact the lives of all Canadians, provides our colleagues a range of opportunities and experiences to help Canadians Live Life Well®.At Loblaw Companies Limited, we succeed through collaboration and commitment and set a high bar for ourselves and those around us. Whether you are just starting your career, re-entering the workforce, or looking for a new job, this is where you belong.Our teams are applying Agentic AI, Generative AI, and Machine Learning to solve complex problems across retail media, advertising, and digital commerce. From intelligent campaign creation and conversational analytics to forecasting, recommendations, and optimization, we're building AI systems that deliver meaningful business impact at scale.We're looking for an Engineering Manager, Agentic AI & Machine Learning to lead a talented team of engineers developing and operating these capabilities.This is an opportunity for a technically strong, hands‑on engineering leader who enjoys working at the intersection of emerging AI technologies, scalable software systems, and real-world business problems. You'll help shape our AI engineering direction, grow a high-performing team, and take ownership of delivering reliable, production‑grade solutions.What You’ll Do:
Lead and grow a high‑performing engineering team: Manage, mentor, and develop talented AI/ML engineers, fostering technical excellence, ownership, collaboration, and continuous learning.Drive Agentic AI innovation: Lead the architecture and development of AI‑powered applications incorporating LLMs, agent orchestration, tool calling, retrieval, and intelligent workflows.Build impactful Machine Learning solutions: Guide the development and productionization of ML models supporting forecasting, recommendations, relevance, targeting, and optimization.Own end‑to‑end engineering delivery: Translate product strategy and business requirements into scalable technical solutions, taking ownership from architecture and development through deployment and ongoing operations.Establish strong AI engineering practices: Champion robust evaluation frameworks, automated testing, observability, and continuous improvement of model quality, response accuracy, latency, reliability, and cost efficiency.Provide technical leadership: Guide architectural decisions, design reviews, and engineering trade‑offs across AI systems, distributed services, data pipelines, and cloud infrastructure. Remain close enough to the technology to help teams navigate complex engineering challenges.Collaborate across functions: Partner closely with Product Management, Data Engineering, Software Engineering, and business stakeholders to define priorities, manage dependencies, and deliver measurable outcomes.Build for production at scale: Promote sound engineering practices across CI/CD, MLOps, LLMOps, security, responsible AI, incident management, and operational readiness.Shape the future of our AI capabilities: Evaluate emerging technologies, challenge existing approaches, and identify opportunities to build reusable engineering foundations that accelerate future product development.Does This Sound Like You?
7+ years of professional experience in Software Engineering, Machine Learning Engineering, or related technical disciplines, including substantial experience building AI/ML-powered systems.2+ years of experience leading and mentoring technical teams, with demonstrated ability to develop engineers, manage delivery, and create a culture of accountability.Proven experience designing, developing, and deploying production‑grade AI/ML solutions, with a strong understanding of scalability, reliability, and operational requirements.Strong understanding of modern AI technologies, including LLMs, Generative AI, agentic workflows, retrieval-augmented generation (RAG), and techniques for evaluating AI system quality.Solid foundation in applied Machine Learning, including model development, evaluation, experimentation, and production deployment.Strong software engineering fundamentals, with proficiency in Python and experience building APIs, distributed systems, and cloud‑native applications.Familiarity with modern AI/ML infrastructure, including model serving, orchestration, automated deployment, monitoring, and observability.Demonstrated ability to lead complex technical initiatives, balance short‑term delivery with long‑term architectural decisions, and navigate ambiguity.Excellent communication and stakeholder management skills, with the ability to explain complex technical concepts, influence decisions, and collaborate across engineering and product teams.Nice to Have:
Experience building production agentic systems using framewo
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