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AI Solutions Engineer

  • Vancouver, BC, Canada

The salary range for this position is $125,000 - $145,000.

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Job details

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

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.

More about this job

Responsibilities

You will build and deploy end-to-end AI assistants, agents, and workflow automations while maintaining high standards for reliability, safety, and traceability. You will also partner with Data Engineering and MLOps teams to ensure production-ready deployments and effective data retrieval pipelines.

Requirements

Candidates must have strong Python skills and hands-on experience shipping LLM applications, including RAG, agent frameworks, and enterprise AI platforms. You should possess a delivery-oriented mindset with the ability to write clear technical documentation and manage complex AI workflows.

Skills

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

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

Categories

  • Technology
  • Data & Analytics
  • Software
  • Finance & Accounting
  • Engineering

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
  • Adversarial Testing
  • UAT
  • Production-ready
  • Traceability
  • Maintainability
  • Asset Management
  • Software Development
  • Artificial Intelligence
  • Technical Documentation

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