Staff AI Engineer (Data & Intelligence function)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff AI Engineer (Data & Intelligence function) based in Canada.

This is a hands-on senior engineering role focused on turning large-scale operational, product, and customer data into intelligent systems and actionable insights. You will build retrieval, knowledge graph, inference, and agentic capabilities on top of a modern data platform. The role spans data, observability, AI, and platform engineering, with the scope evolving alongside organizational priorities. You will create shared capabilities that multiple teams can rely on to build intelligent products and workflows. Much of the work involves designing and shipping production systems while establishing technical patterns and standards for other engineers. You will work closely with product, platform, engineering, and business leaders in an environment that values rapid iteration and practical experimentation. This is an opportunity for a senior individual contributor to shape AI engineering practices while delivering measurable business impact.

Accountabilities:

• Design, build, test, and ship production AI systems as an active hands-on engineering contributor.
• Lead the development of high-performance customer context retrieval capabilities using semantic search, indexing, embeddings, vector search, hybrid retrieval, and knowledge graph approaches.
• Build systems that allow agents to identify customers and retrieve comprehensive context efficiently from multiple data sources.
• Develop inference and signal-processing layers that read from an Iceberg-based lakehouse, generate scored insights, and expose reliable signals through contracts that other teams can consume.
• Transform signals such as churn risk, usage or entitlement mismatches, and expansion opportunities into automated workflows that drive appropriate actions, incorporating human review where required.
• Select the appropriate technical approach for each workload, balancing large language models, smaller specialized or fine-tuned models, and deterministic software while managing inference costs.
• Establish evaluation and observability practices for AI systems, including tracing, prompt and model versioning, dataset-driven testing, and evidence that signals are reliable before they are acted upon.
• Define reusable engineering patterns and shared capabilities that enable other teams to build effectively on the organization’s AI and data infrastructure.
• Partner with engineering and business leaders to translate strategic objectives into scalable, production-ready systems.
• Adapt quickly to unfamiliar technical contexts, technologies, codebases, and evolving AI frameworks while maintaining a strong focus on delivering useful production outcomes.

Requirements:

• You have 8+ years of software engineering experience, including at least 3 years shipping AI or machine learning systems into production.
• You have strong software engineering fundamentals and deep proficiency in at least one language used for AI and data engineering, with Python being the primary language for ML and insights.
• You are highly proficient with data at scale, including advanced SQL, a transformation framework such as dbt, and distributed query engines operating over lakehouse or warehouse storage.
• You understand query optimization, partitioning, cost management, and the practical challenges of working with large-scale data platforms.
• You have production experience with retrieval and context engineering, including embeddings, vector search, hybrid or graph retrieval, and measuring retrieval quality and effectiveness.
• You have experience modeling entities and relationships across multiple data sources, particularly where this supports customer context and intelligent systems.
• You have built and operated agentic systems and durable workflows using tool calling, state, memory, and human-in-the-loop patterns, with experience in Temporal, LangGraph, or equivalent technologies.
• You understand how to evaluate and observe AI systems through tracing, prompt and version management, dataset-driven testing, and other appropriate engineering practices.
• You have cloud deployment experience, preferably with AWS, including containerized services and responsibility for managing inference costs.
• You hold a B.S. in Computer Science or have equivalent practical experience.
• You have experience creating shared data or ML capabilities that other engineering teams can build upon.
• Experience with smaller or fine-tuned models, model distillation, high-volume observability data, OpenTelemetry, data governance, lineage, access controls, or data residency is highly valuable.
• Experience in enterprise SaaS, CMS environments, or complex digital platforms is beneficial.
• You are comfortable using AI-assisted development tools such as Claude, Cursor, or GitHub Copilot and technologies such as MCP to connect intelligent systems with real-world tools and services.
• You communicate complex AI system designs effectively to both technical teams and executive stakeholders.
• You have a strong senior individual contributor track record, with a reputation for high-quality code and system design and an ability to mentor others through technical leadership and example.
• You demonstrate adaptability, intellectual curiosity, strong ownership, and a builder mentality, with the ability to use AI as an integral part of your engineering workflow.

Benefits:

• Competitive healthcare coverage designed to support employees and their families.
• Wellness programs and resources supporting physical and mental well-being.
• Flexible time-off policies that allow employees to take time away when needed.
• Parental leave and family-focused support.
• Recognition programs that celebrate employee contributions and achievements.
• Opportunities to work with large-scale data platforms, AI systems, agentic workflows, and modern cloud technologies.
• Exposure to complex enterprise data, observability, retrieval, inference, and intelligent automation challenges.
• Significant opportunity to influence shared engineering capabilities and AI development practices across multiple teams.
• A hands-on environment that supports experimentation, continuous learning, and rapid iteration.
• Career development opportunities within a global technology organization focused on innovation and AI-enabled transformation.