AI Solutions Engineer

Urban Ridge Supplies; Toronto; Hybrid; CAD 125,000 - 145,000; Full time

Job summary

Connor, Clark & Lunn Financial Group in Canada is hiring an AI Solution Engineer to build production-ready AI assistants and workflow automations in a hybrid team. You will own retention, quality, safety, and maintainability while partnering with Data Engineering and MLOps to ship responsibly in a regulated financial services environment.

The role emphasizes end-to-end LLM apps, retrieval pipelines, and prompt engineering with collaborations across affiliate teams.

Qualifications

• Strong Python skills with end-to-end shipping of LL M apps.
• Hands-on experience with retrieval, vector search and RAG.
• Experience with agent frameworks and multi-step workflows.
• Familiarity with enterprise AI platforms and cost/performance trade-offs.
• Knowledge of prompt engineering, constraints and failure modes.
• Ability to work with semi-structured data and translate to retrieval assets.
• Proven ability to document, test and deliver production-quality work.

Responsibilities

• Build AI assistants and agents end-to-end from a signed-off spec.
• Design and maintain retrieval pipelines and metadata schemas.
• Engineer prompts, test, and document failure modes and edge cases.
• Own code quality, versioning, tests, and clean documentation.
• Collaborate with Data Engineering to ensure data readiness.
• Deploy via MLOps pipelines with monitoring and runbooks.
• Partner with affiliate teams, demo increments, capture feedback.
• Document limitations, risks, and mitigations before UAT.

Skills

Python

RAG experience

Agent frameworks

Enterprise AI platforms

Prompt engineering

Semi-structured data

Documentation & delivery

Tools

LangChain

LlamaIndex

Azure OpenAI

Google Vertex AI

Anthropic APIs

Vector databases

Job description

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

The salary range for this position is $125,000 - $145,000. The salary range provided reflects the base salary range for this position as required by legislation. In addition, there is an annual performance bonus which contributes to the total compensation for this position. Further questions may be directed to the HR team during the interview process.

For a closer look at how you can build your career with us, we invite you to explore cclgroup.com.

We are committed to providing an inclusive, accessible recruitment and selection process. We welcome and encourage applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the recruitment and selection process. Please contact [e-mail] if you require accommodation.

AI may be used to support certain stages of our screening & recruitment process. These tools support, but do not replace, human judgment and decision-making.