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About the Role

Build the AI systems that Connor, Clark & Lunn Financial Group and its affiliate teams use in day-to-day work. Turn signed-off specifications into production-ready AI assistants, agents, and workflow automations. Own build quality, reliability, safety, traceability, and maintainability, partnering closely with Data Engineering and MLOps to ship responsibly in a regulated financial services environment.

Responsibilities

• Build AI assistants and agents end-to-end from a signed-off spec, retrieval, tool integrations, prompt logic, source citation, and workflow integration
• Design and maintain retrieval pipelines, chunking strategy, metadata schema, indexing, access controls, and query optimization
• Engineer prompts with discipline, write, test, evaluate, and iterate; document failure modes and edge cases
• Own code quality and handoff, version artifacts, write tests where appropriate, and maintain clean, reviewable documentation
• Partner with Data Engineering to make data retrieval-ready, define ingestion needs, document assumptions, and validate data quality impacts
• Deploy through standard MLOps pipelines, monitoring/alerting, rollback readiness, cost controls, and operational runbooks
• Collaborate with affiliate teams during builds, demo real increments, capture feedback, and incorporate changes without breaking scope
• Document known limitations, risks, and mitigations before UAT, set expectations and prevent surprises for business stakeholders

Requirements

• Strong Python skills with experience shipping LLM applications end-to-end (build, test, deploy, and operate)
• Hands-on RAG experience, document processing, vector databases/search, and retrieval evaluation (precision/recall, grounding quality)
• Experience with agent frameworks (e.g., LangChain, LlamaIndex or equivalents), including tool use, orchestration, and multi-step flows
• Experience on enterprise AI platforms (e.g., Azure OpenAI, Google Vertex AI, Anthropic APIs), including security and cost/performance trade-offs
• Prompt engineering fundamentals, structured prompting, output constraints, adversarial/failure-mode testing, and reproducibility
• Comfort working with semi-structured/unstructured data (PDFs, financial docs, emails, notes) and translating it into retrieval-ready assets
• Delivery mindset and strong written communication, hold scope, write clear technical documentation, and finish to production-quality

Work Type

• Hybrid

Salary/Compensations

• $125,000 - $145,000

Benefits

• Annual performance bonus

About the Company

• Connor, Clark & Lunn Financial Group is one of Canada’s top-performing asset managers.
• CC&L Financial Group is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer.

Equal Opportunity

• All qualified applicants will receive consideration for employment without regard to gender, ethnicity, religion, sexual orientation or expression, disability, or age.
• AI is not used in the screening, assessment or selection of applications at this time.