AI Solutions Engineer

Interested in joining one of Canada’s top-performing asset managers? We’re hiring an AI Solution Engineer in our AI Solutions engineering team. You build the AI systems that Connor, Clark & Lunn Financial Group and our affiliate teams use in day-to-day work. You turn signed-off specifications into production-ready AI assistants, agents, and workflow automations. You own build quality, reliability, safety, traceability, and maintainability, and partner closely with Data Engineering and MLOps to ship responsibly in a regulated financial services environment. We operate on a hybrid model with three days a week in-office to facilitate team collaboration.

What You Will Do

• 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

What You Will Bring

• 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