Principal Machine Learning Engineer

Company Description Wiser Solutions is a suite of in-store and eCommerce intelligence and execution tools. We're on a mission to enable brands, retailers, and retail channel partners to gather intelligence and automate actions to optimize pricing, marketing, and operations initiatives, both in-store and online. Our Commerce Execution Suite is available globally. Job Description LOCATION: This position can be based anywhere in Canada, with a preference for someone in the eastern or central time zone who can work with our teams in the US, Europe and India. ABOUT THE ROLE  Wiser Solutions is seeking a Principal Machine Learning Engineer to shape and execute our AI and data science strategy. This is a senior technical leadership role for someone who combines deep expertise in machine learning, data science, and production engineering with the business acumen to translate complex capabilities into customer value. You will be the technical authority for AI at Wiser: defining architectural direction, representing our capabilities to customers and partners, and delivering production systems that drive measurable business outcomes. This role requires someone who operates fluidly between strategic planning and hands-on implementation, who can present to executives and debug production pipelines in the same week. We're building an AI-native engineering culture at Wiser, one where AI tools and techniques are woven into how we work, not just what we build. We need a Principal AI Engineer who doesn't just deliver AI products but models AI-augmented ways of working and helps the broader engineering organization adopt them. If you believe AI is fundamentally transforming software development and you're already living that transformation daily, we want to talk. What You Will Do Strategic Leadership • Define and evolve Wiser's AI and data science technical strategy in partnership with product and business leadership • Represent Wiser's AI capabilities to customers, partners, and advisors—articulating our approach, roadmap, and differentiation • Identify high-impact opportunities where AI can solve customer problems or create competitive advantage • Establish technical standards, patterns, and best practices that influence engineering decisions across the organization Technical Execution • Architect and build production AI systems including LLM applications, RAG pipelines, semantic search, and traditional ML models • Design rigorous evaluation frameworks, experimentation methodologies, and monitoring systems that ensure AI solutions deliver reliable, measurable results • Bridge classical data science approaches (statistical modeling, experimentation design, feature engineering) with modern generative AI techniques • Own technical quality for AI systems end-to-end: from data pipelines through model deployment to production observability Cross-Functional Impact • Partner with product management to translate business requirements into technical approaches and validate solutions against customer needs • Mentor and elevate the AI/data science team (3-5 engineers), raising the technical bar through code review, architecture guidance, and knowledge transfer • Collaborate across engineering teams to integrate AI capabilities into Wiser's broader platform architecture • Drive build-vs-buy decisions and vendor evaluations for AI infrastructure and tooling • Champion AI-native development practices across Wiser engineering—demonstrating how AI tools accelerate development, improve code quality, and change what's possible • Help build an engineering culture where AI augmentation is the default, not the exception Qualifications • 15+ years of experience in data science, machine learning, or ML engineering, with demonstrated progression into technical leadership • Deep expertise in statistical methods, experimental design, and classical ML (not just LLM integration) • Proven ability to architect and deliver production ML/AI systems at scale on cloud platforms (AWS strongly preferred) • Strong software engineering fundamentals: you write production-quality code, not just notebooks • Track record of organization-wide technical influence—setting standards, driving architectural decisions, mentoring engineers • Experience communicating technical strategy and capabilities to non-technical stakeholders, including customers and executives • Demonstrated ability to operate autonomously, identify high-impact problems, and drive initiatives without close direction • Active, daily use of AI coding assistants (Cursor, Claude Code, GitHub Copilot) and a demonstrated belief that AI fundamentally changes how software gets built Technical Depth Expected: • NLP and text analytics: embeddings, semantic similarity, classification, entity extraction, and information retrieval • LLM integration patterns: prompt engineering, RAG architectures, agent frameworks, evaluation methods • Data engineering: pipeline design, data quality, feature stores,