Jobs in Canada

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Added 17 hours ago
MG
Senior Software Developer / Développeur de logiciels principal
Maples Group
Montreal, QC
- Full-time
We are looking for an accomplished Senior Software Developer to join our Business Technology team as part of our financial services business in Montreal.
Added 18 hours ago
PC
Principal Software Engineer
Priority Commerce
Waterloo, ON
- $191,000 to $249,000 CAD / year
- Full-time
Job title: Principal Software Engineer - Innovation Studio
Added 18 hours ago
TM
Senior Software Developer / Développeur de logiciels principal
The Maples Group (Financial Services)
Montreal, QC
- Full-time
We are looking for an accomplished Senior Software Developer to join our Business Technology team as part of our financial services business in Montreal.
Added 22 hours ago
SP
Principal Applied AI Architect
Salesforce Partner Canada
Montreal, QC
- Full-time
As a Principal or Distinguished Applied AI Architect, you will be the trusted technical lead for a set of strategic accounts. You’ll help design agentic AI architectures that hold up under…
Added 1 day ago
S
Principal Applied AI Architect
Salesforce
Toronto, ON · Montreal, QC · Canada - Montreal
- Full-time
As a Principal or Distinguished Applied AI Architect, you will be the trusted technical lead for a set of strategic accounts. You’ll help design agentic AI architectures that hold up under…
Added 3 days ago
A
Software Architect: AEC Platform Data
Autodesk
Quebec, QC
- Full-time
…AEC Data, qui occupe également le poste d’architecte logiciel principal. La responsabilité principale consiste à définir la manière dont plusieurs équipes conçoivent et développent - par le…
Added 3 days ago
A
Software Architect: AEC Platform Data
Autodesk
Montreal, QC
- Full-time
…AEC Data, qui occupe également le poste d’architecte logiciel principal. La responsabilité principale consiste à définir la manière dont plusieurs équipes conçoivent et développent - par le…
Added 3 days ago
A
Software Architect: AEC Platform Data
Autodesk
Toronto, ON
- Full-time
…AEC Data, qui occupe également le poste d’architecte logiciel principal. La responsabilité principale consiste à définir la manière dont plusieurs équipes conçoivent et développent - par le…
Added 6 days ago
A
Sr AI Architect
Accenture
Ottawa, ON · Toronto, ON · Montreal, QC
- Full-time
As a Lead or Principal AI Architect, you will serve as the definitive technical authority on AI architecture within client engagements and across the practice. You will own the complete,…
Added 6 days ago
A
Principal MCP/AI Developer
Autodesk
Toronto, ON · ON · AMER - Canada - Ontario - Toronto - University Ave
- Full-time
As the Principal MCP/AI Developer, you will define and drive the technical direction of that platform - its capability/tool model, agent-orchestration architecture, trust model, and the way…
Added 7 days ago
A
Senior Principal Engineer
Autodesk
Toronto, ON
- Full-time
GET Engineering is seeking an experienced Senior Principal Engineer – AI and Engineering Excellence to lead the technical strategy and evolution of our cloud platform, developer experience,…
Added 8 days ago
IG
Principal Platform Engineer
Insight Global
Vancouver, BC
- Full-time
Insight Global is seeking a Principal Platform Engineer for a global retailer to take technical ownership of and scale an enterprise AI platform within GCP.
Added 12 days ago
AA
Principal Product Manager
Asensean Advisory
Remote, Canada
- $150,000 to $190,000 CAD / year
- Full-time
- Remote
We are seeking a Principal Product Manager to lead product strategy and execution across multiple products and initiatives. You will own customer problems and product outcomes from…
Added 13 days ago
A
Forward Deployed Solutions Lead
Accenture
Calgary, AB · Ottawa, ON · Toronto, ON · Montreal, QC
- Full-time
Build and integrate Model Context Protocol (MCP) servers and tools.
Added 14 days ago
T
SAP AI Orchestration Developer
Talan
Toronto, ON · Montreal, Québec
- $140,000 to $200,000 CAD / year
- Full-time
Reporting to the Principal Director, SAP Innovation and IP and the Vice President, Delivery for engagement assignment, the SAP AI Orchestration Developer designs and builds AI-enabled…
Added 18 days ago
PR
Senior/Principal LLM & Evals Engineer
Perennial Resources International
Toronto, ON
- Full-time
Build or integrate MCP servers and other interfaces that allow models to query enterprise data, research systems, analytical tools, and external services securely.
Added 19 days ago
TD
Senior Solutions Architect (Pre-Sales)
Teifi Digital
Vancouver, BC
- $110,000 to $130,000 CAD / year
You'll join a growing architecture team of Principal, Solutions and Systems Architects, working alongside our Account Executives and reporting to the Director of Solutions Architecture.
Added 19 days ago
Senior Pre-Sales Solutions Architect – AI Agent & Workload Security
Tigera
Vancouver, BC
…govern them, and Lynx is how we help them do it. We’re hiring a Principal Solutions Architect in Vancouver to act as the number two for Lynx presales across North…
Added 21 days ago
7
Senior Software Developer, Developer Experience
7shifts
Remote, Canada
- $145,000 to $190,000 CAD / year
- Full-time
- Remote
As an Senior Software Developer on the Developer Experience squad at 7shifts, you work at the intersection of strong engineering judgment and AI-enabled execution. You report to the…
Added 22 days ago
RB
Principal AI Engineer, AI DLC Platform
Royal Bank of Canada
Toronto, ON
- Full-time
As Principal AI Engineer you will own the technical direction of that journey. You will define the platform architecture, the production boundary between code that runs in production and…
Added 22 days ago
R
Principal AI Engineer, AI DLC Platform
RBC
Toronto, ON
- Full-time
As Principal AI Engineer you will own the technical direction of that journey. You will define the platform architecture, the production boundary between code that runs in production and…
Added 24 days ago
T
SAP AI Orchestration Developer
Talan
Toronto, ON · Montreal, Québec
- $140,000 to $200,000 CAD / year
- Full-time
Reporting to the Principal Director, SAP Innovation and IP and the Vice President, Delivery for engagement assignment, the SAP AI Orchestration Developer designs and builds AI-enabled…
Added 25 days ago
A
Principal MCP/AI Developer
Autodesk
ON · Ontario, Canada
- Full-time
As the Principal MCP/AI Developer, you will define and drive the technical direction of that platform - its capability/tool model, agent-orchestration architecture, trust model, and the way…
Added 25 days ago
D
Senior Solar Energy Analyst
DNV
Montreal, QC
- Full-time
…Energy Systems est actuellement à la recherche d’un(e) analyste principal(e) en énergie solaire pour rejoindre son équipe d’évaluation de l’énergie solaire. Dans ce rôle, vous ferez partie…
Added 25 days ago
A
Principal MCP/AI Developer
Autodesk
Toronto, ON
- Full-time
As the Principal MCP/AI Developer, you will define and drive the technical direction of that platform - its capability/tool model, agent-orchestration architecture, trust model, and the way…

Perennial Resources International

Senior/Principal LLM & Evals Engineer

Toronto, ON, Canada

Full-time

Apply on LinkedIn About employer

Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Nov 22, 2026
Posting language: English
Posted: Oct 23, 2026

Original job description

NO H1S OR 3RD PARTIES.

We are seeking an experienced GenAI engineer with deep expertise in LLM evaluation, production-grade AI systems, and financial services. The successful candidate will design and improve reliable AI applications that answer complex financial questions using structured and unstructured data.

This is primarily an LLM engineering role - not a trading or quantitative research position. However, candidates must understand front-office financial workflows and the standards of accuracy, reliability, and responsiveness expected by investment professionals.

Ideal candidates may come from financial-data and AI platforms, banks, asset managers, fintech companies, or specialist firms such as Rogo, RavenPack, LSEG, FactSet, Bloomberg, S&P Global, or similar organizations.

Key responsibilities

Design comprehensive evaluation frameworks for LLM-powered financial applications, covering factual accuracy, relevance, completeness, grounding, consistency, and appropriate refusal behavior.
Build representative evaluation datasets and financial “golden sets,” including difficult, ambiguous, adversarial, and business-critical test cases.
Establish automated evaluation pipelines combining deterministic tests, model-based evaluation, human review, and expert financial validation.
Identify and reduce hallucinations, unsupported conclusions, citation errors, retrieval failures, and other sources of unreliable output.
Develop production-grade applications that answer questions using structured and unstructured financial information, including research, filings, transcripts, news, market data, and proprietary documents.
Design and optimize retrieval, RAG, search, ranking, reranking, context construction, and tool-use workflows.
Build or integrate MCP servers and other interfaces that allow models to query enterprise data, research systems, analytical tools, and external services securely.
Evaluate the complete application rather than the model in isolation, including retrieval quality, tool selection, tool execution, context quality, answer generation, citations, and end-to-end task success.
Implement monitoring and observability for production AI systems, including quality degradation, data drift, latency, cost, failure modes, and user feedback.
Apply fine-tuning, supervised learning, preference optimization, synthetic-data generation, and related techniques where they provide measurable improvements.
Develop bespoke smaller language models through model distillation - for example, transferring capabilities from a large teacher model to a smaller model optimized for a particular financial domain or workflow.
Make informed decisions about when to use prompting, retrieval, tools, fine-tuning, distillation, or a combination of techniques.
Optimize models and applications for accuracy, latency, scalability, inference cost, security, and operational reliability.
Work closely with financial-domain experts, product teams, data engineers, and application engineers to translate front-office requirements into measurable AI capabilities.
Define production-readiness criteria, release gates, regression tests, and quality thresholds for new models and application changes.

Required experience

Significant hands-on experience building and operating LLM or GenAI applications in production.
Deep knowledge of LLM evaluation methods, benchmarks, test-set design, error analysis, regression testing, and continuous quality measurement.
Demonstrated ability to improve the factual accuracy and reliability of answers generated from enterprise or domain-specific information.
Strong experience working with unstructured information and document-heavy workflows.
Practical expertise in retrieval-augmented generation, semantic and hybrid search, embeddings, reranking, grounding, citations, and context management.
Experience building tool-using or agentic systems, ideally including MCP servers or comparable model-to-data and model-to-tool interfaces.
Hands-on experience with model fine-tuning, distillation, or the development of domain-specific small and large language models.
Strong understanding of the trade-offs among model quality, model size, latency, throughput, infrastructure requirements, and inference cost.
Experience implementing production monitoring, evaluation pipelines, quality controls, and safeguards for probabilistic AI systems.
Meaningful financial-services experience, with a working understanding of front-office users, terminology, data, and workflows.
Familiarity with how investment professionals consume research, interrogate financial information, evaluate evidence, and make time-sensitive decisions.
Strong software-engineering skills and the ability to build robust, testable, maintainable production systems.

Financial-domain expectations

Candidates should understand at least several of the following:

Equity, fixed-income, credit, commodities, foreign-exchange, or multi-asset workflows.
Financial research, company analysis, market intelligence, news analytics, and investment decision support.
Company filings, earnings transcripts, financial statements, estimates, corporate actions, and market data.
The distinction between facts, estimates, opinions, forecasts, and model-generated conclusions.
Data entitlements, auditability, source attribution, information security, and regulatory or compliance considerations.
The importance of timeliness, point-in-time correctness, provenance, and reproducibility in financial applications.

Direct experience as a trader or quant is not required. The essential requirement is sufficient domain fluency to understand front-office use cases and recognize when an AI-generated answer is incomplete, misleading, unsupported, or financially implausible.

Core competencies

LLM evaluation: Can define what “good” means, measure it rigorously, and create repeatable systems for improving quality.
Production AI engineering: Can take an LLM application from prototype to a reliable, observable, scalable production service.
Financial-domain fluency: Understands front-office users, financial information, relevant workflows, and the consequences of inaccurate output.
Grounded answer generation: Knows how to produce accurate, traceable responses supported by authoritative sources.
Retrieval and data integration: Can connect models effectively to structured data, unstructured content, search systems, tools, and enterprise platforms.
Model optimization: Understands fine-tuning, distillation, model selection, and the construction of specialized models for defined tasks.
Analytical problem-solving: Can diagnose whether failures originate in the underlying data, retrieval, tool use, context construction, prompting, model behavior, or application logic.
Risk and quality mindset: Anticipates edge cases and designs controls appropriate for high-stakes financial applications.
Cross-functional collaboration: Communicates effectively with financial experts, engineers, researchers, product leaders, and senior stakeholders.
Outcome orientation: Focuses on measurable user and business outcomes rather than model demonstrations alone.

Preferred experience

Experience at a financial-data provider, investment-research platform, financial AI company, bank, asset manager, hedge fund, or capital-markets technology business.
Experience supporting research analysts, portfolio managers, traders, salespeople, or other front-office professionals.
Experience building domain-specific models or AI applications for financial research and decision support.
Experience with SaaS products, multi-tenant platforms, enterprise deployments, or customer-facing AI applications.
Familiarity with model governance, access controls, data privacy, audit trails, and regulated production environments.
Experience evaluating both proprietary and open-weight models and selecting the appropriate model for a given task.

What success looks like

The successful candidate will build AI systems that financial professionals can use with confidence. Their work will result in measurable improvements in answer accuracy, grounding, coverage, latency, cost, and reliability. They will also establish a disciplined evaluation process that makes quality visible, catches regressions before release, and supports the development of specialized models for high-value financial workflows.

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More about this job

Responsibilities

Design rigorous evaluation frameworks, datasets, and automated quality pipelines for LLM applications serving financial professionals. Build and optimize production AI systems that retrieve and use financial data securely, generate grounded answers, and meet measurable standards for accuracy, reliability, latency, and cost.

Requirements

Requires substantial hands-on experience building and operating production LLM applications, with deep expertise in evaluation, RAG, search, tool use, model optimization, and reliable software engineering. Candidates must also have meaningful financial-services fluency and understand front-office workflows, financial information, and the risks of inaccurate or unsupported answers.

Skills

LLM Evaluation
Production AI Engineering
Financial Services Domain Knowledge
Retrieval-Augmented Generation
Semantic and Hybrid Search
Reranking
Grounded Answer Generation
Evaluation Dataset Design
Model Fine-Tuning
Model Distillation
Agentic Systems
MCP Servers
Production Monitoring and Observability
Financial Data Integration
Software Engineering
Risk and Quality Management

Visa sponsorship

Not detected in the job text

Keywords

Large Language Models
Generative AI
LLM Evaluation
Evaluation Frameworks
Golden Sets
Regression Testing
Retrieval-Augmented Generation
Semantic Search
Hybrid Search
Embeddings
Reranking
Grounding
Citations
MCP Servers
Agentic Systems
Fine-Tuning
Model Distillation
Synthetic Data
Model-Based Evaluation
Human Review
Financial Research
Market Data
Company Filings
Earnings Transcripts
Financial Statements
Data Entitlements
Auditability
Data Provenance
Model Governance
Production Monitoring

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Jobs in Canada