Interested in joining one of Canada’s top performing asset managers? We are looking for an AI Solutions Engineer for a new full-time position on our Connor, Clark & Lunn Private Capital team. In this role, you will design, build, test and improve practical AI-enabled tools and workflows that help Private Capital teams better serve high-net-worth clients, advisors and internal stakeholders.

Our use cases are focused on real business value: improving the advisor and client experience, making information easier to find and use, reducing manual servicing and operations work, and strengthening consistency, controls and governance across complex processes. This is a hands-on role focused on generative AI, large language models and agent-based workflows. You will work closely with Private Capital, Technology, Data, Risk and Compliance to turn business needs into secure, useful and well-governed solutions that support areas such as sales, client servicing, documentation, reporting, approvals and compliance.

If you enjoy building practical tools, solving workflow problems, learning how a business works, and moving ideas from prototype to scalable solution, this could be a great opportunity. Our offices operate on a hybrid model with three days a week in-office.

What You Will Do

Work with business teams to understand processes, pain points, data sources and user needs.

Requirements

into practical solution designs, including user flows, prompts, data inputs, retrieval approaches, review steps and success measures. Collaborate across Private Capital, Technology, Data, Risk, and Compliance to deliver secure, practical and well-governed AI solutions. Design, build, and configure AI-enabled tools, assistants, agents, and workflow automation.

Connect AI solutions to relevant business systems, documents, internal knowledge sources, and APIs where appropriate. Use techniques such as prompt design, retrieval-augmented generation, structured outputs, tool calling, evaluation and guardrails. Test and improve outputs using user feedback, structured checks and clear acceptance criteria.

Document solution designs, assumptions, risks, controls, and operating procedures. Support the transition of successful prototypes into scalable, maintainable solutions. What You Bring 3-5+ years of experience in software engineering, data science, automation, technical consulting, solutions engineering, or a related field.

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). , LangChain, LlamaIndex or equivalents), including tool use, orchestration, and multi-step flows.

, 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.

Experience working with Salesforce, including Agentforce and/or connecting Salesforce with other AI platforms. Strong understanding of how to connect AI tools to business data, documents, applications and internal knowledge sources. Strong analytical, problem-solving, and communication skills, with the ability to work through ambiguity and explain technical concepts to both technical and non-technical stakeholders.

Bachelor’s degree in computer science, Engineering, Data Science, Mathematics, Information Systems, or a related technical field, or equivalent practical experience. Accountability, initiative, and comfort working independently while collaborating closely with business and technical teams.