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Added 8 mins ago
[C] Salesforce Engineer / Developer
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Toronto, ON
Part-time
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Added 8 mins ago
Senior Conversation Bot Engineer - Dialogflow CX
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Full-time
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Added 8 mins ago
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Added 8 mins ago
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Added 37 mins ago
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Added 38 mins ago
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Added 1 hour ago
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Added 1 hour ago
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Added 1 hour ago
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Added 2 hours ago
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Added 2 hours ago
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Connor, Clark & Lunn Financial Group Ltd.

AI Solutions Engineer

Toronto, ON, Canada

Pay: The salary range for this position is $125,000 - $145,000.
Posting language: English
Posted: Sep 28, 2026

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Original 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.

What We Offer
At Connor, Clark & Lunn Financial Group, we’re "invested in our greatest asset, you". Our shared goals reflect what matters most to our people:

Your community: we foster belonging through collaboration, inclusion and flexibility. Enjoy social events, volunteering and a strong commitment to corporate social responsibility. Our flexible work practices are designed to support work-life balance and provide employees with flexibility in where and how they work
Your growth: we prioritize your development with meaningful work, continuous learning budgets, paid study days and support for professional designations.
Recognizing you: your contributions are rewarded with competitive total compensation, retirement savings support, wellness credits, enhanced parental leave, and comprehensive health benefits.

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 talentacquisition@cclgroup.com 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.

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More about this job

Responsibilities

The AI Solutions Engineer will build and deploy production-ready AI assistants, agents, and workflow automations from signed-off specifications. They will also partner with Data Engineering and MLOps teams to ensure system reliability, traceability, and data quality within a regulated financial environment.

Requirements

Candidates must have strong Python skills and hands-on experience shipping LLM applications, including RAG, agent frameworks, and enterprise AI platforms. The role requires a delivery-oriented mindset with the ability to write clear technical documentation and manage complex data retrieval pipelines.

Skills

Python
LLM applications
RAG
Vector databases
LangChain
LlamaIndex
Prompt engineering
Azure OpenAI
Google Vertex AI
Anthropic APIs
MLOps
Data engineering
Document processing
Workflow automation
Technical documentation
API integration

Benefits

Retirement savings support
Wellness credits
Enhanced parental leave
Comprehensive health benefits
Continuous learning budgets
Paid study days

Visa sponsorship

Not detected in the job text

Keywords

AI Solutions Engineer
Python
LLM
RAG
LangChain
LlamaIndex
Azure OpenAI
Google Vertex AI
Anthropic
MLOps
Vector Databases
Prompt Engineering
Data Engineering
Workflow Automation
Financial Services
API
Retrieval Pipelines
Metadata Schema
Indexing
Access Controls
Query Optimization
Version Control
UAT
Technical Documentation
Asset Management
Hybrid Work
Software Development
Artificial Intelligence
Machine Learning
System Reliability

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