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Added 6 mins ago
T
Applied AI/ML Scientist
TEEMA
Vancouver, BC
- $100,000 to $120,000 CAD / year
- Full-time
Applied AI/ML Scientist / Engineer, Health Agents
Added 37 mins ago
AL
Principal Product Applications Engineer - Scorpio
Astera Labs
Vancouver, BC
- Full-time
…accelerators at rack scale. As a Principal Product Applications Engineer on the Scorpio team, you will be the technical face of our switching portfolio to the world's leading hyperscalers,…
Added 37 mins ago
T
Senior Data Engineer
TekRek
Montreal, QC
- $140,000 to $160,000 CAD / year
- Full-time
As a Senior Data Engineer, you'll own data platform design and delivery for client engagements, from scoping and architecture through to production. You'll work directly with client…
Added 1 hour ago
BC
Senior Manager, Data & AI
BDO Canada
Vancouver, BC
- Full-time
Architect and oversee the delivery of modern data platforms that bring data together from source systems, create trusted data models, enable reporting and analytics, and support AI…
Added 1 hour ago
T
AI/ML Engineer
TEEMA
Vancouver, BC
- $110,000 to $120,000 CAD / year
- Full-time
Technology: Experience with agentic LLM systems.
Added 1 hour ago
CA
Brand Engineer
Calico AI
Toronto, ON
- Full-time
…of it, is a hands-on job. That is why we are hiring a Brand Engineer: someone who sits with the customer, maps how they work today, and builds the path from first pilot to running their…
Added 1 hour ago
Senior Staff Software Engineer
Diligent Corporation
Vancouver, BC
As a Senior Staff Software Engineer on Diligent's Platform AI team, based out of our technical center of excellence in Vancouver, you will drive the architecture and technical direction of…
Added 1 hour ago
AC
Product Development Engineer – Liquid Cooling
Amphenol Communications Solutions
Markham, ON
- Full-time
…electrical, electronic and fiber optic connectors and interconnect systems, antennas, sensors and sensor-based products and coaxial and high-speed specialty cable. ACS has an expansive…
Added 2 hours ago
Agentic AI Software Backend & Platform Engineer
Siemens
Kitchener, ON
We are seeking a Backend & AI Engineer, for Siemens Digital Industries Operations Software to help build and scale our Agentic AI platform. The role focuses on solid backend engineering,…
Added 2 hours ago
Dynamics/Powerplatform engineer
Capgemini
Montreal, QC
We are looking for a Senior Microsoft power platform Engineer, who is familiar with AI Builder and Co-Pilots as well as Dynamics and Power Apps. The ideal candidate will have experience…
Added 3 hours ago
AG
Kubernetes (K8s) / HPC Engineer
Axiom Global Technologies
Canada
- Contract
We are seeking an experienced Kubernetes (K8s) / High-Performance Computing (HPC) Engineer to design, deploy, optimize, and manage scalable compute platforms supporting AI/ML, engineering…
Added 3 hours ago
Senior Machine Learning Engineer, Recommendations
Lyft
Toronto, ON
We are seeking a Senior Machine Learning Engineer to join the Rider Applied AI team and lead the design, development, and deployment of state-of-the-art machine learning and artificial…
Added 3 hours ago
Senior Software Engineer, Full-Stack Applications - Python
Fitch Group
Toronto, ON
Senior Software Engineer, Full-Stack Applications – Python
Added 3 hours ago
R
Architecte de données / Data Architect
RTX
Longueuil, QC
- Full-time
Added 3 hours ago
Sr Software Dev Engineer III, RDS for Oracle
Amazon
Vancouver, BC
Amazon Web Services is hiring a Software Development Engineer III for the RDS for Oracle team. We are looking for an experienced engineer to design and build the next generation of managed…
Added 4 hours ago
Senior Software Engineer, Data Platform
Okta
Toronto, ON
The Data Platform team is responsible for the foundational data services, systems, and data products for Okta that benefit our users. Today, the Data Platform team solves challenges and…
Added 11 hours ago
M
AI Engineer
maru.ai
Engineer, BC
- Full-time
We are an early stage startup developing cutting-edge AI technology. We are looking for a talented AI engineer to join our small but growing team. You will have the opportunity to take on a…
Added 11 hours ago
M
Software QA Engineer
maru.ai
Engineer, BC
- Full-time
We are looking for a detail-oriented Software QA Engineer to join our fast-growing AI/ML startup. In this role, you will help ensure the quality of our AI/ML products by designing and…
Added 11 hours ago
M
Software Engineer
maru.ai
Engineer, BC
- Full-time
Develop and maintain backend systems and architecture using Go and Python
Added 11 hours ago
J
Machine Learning Engineer Intern
Jobright.ai
Canada
- Internship
…helps users reach better job outcomes. The Machine Learning Engineer Intern will develop and evaluate machine-learning features, support data pipelines and integrations, investigate model…
Added 11 hours ago
A
Manager, Software Engineering
AltaML
Toronto, ON
- Full-time
We overpay for the right people. We'd rather pay a premium for an engineer who embodies this and multiplies the team than save money on someone who doesn't.
Added 12 hours ago
E
Principal Product Manager - Identity Threat Detection and Response
Elastic
Canada
- Full-time
…behavioral analytics, anomaly detection, LLMs, and agentic systems. You are comfortable working closely with data scientists and engineers, and you treat AI agents both as a subject to be…
Added 12 hours ago
M
Security Engineer II - Identity and Access Management
Marqeta
Remote, Canada
- Full-time
- Remote
We’re seeking an experienced Security Engineer II with a strong passion for Identity and Access Management (IAM) and expertise in cloud-native environments, particularly AWS. You'll build,…
Added 15 hours ago
B&
Lead, AI Engineering
Bain & Company
Zurich, ON
- Full-time
The Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle - from early…
Added 16 hours ago
AI
Kubernetes Engineer
Aptino, Inc.
Canada
- Contract
We are looking for an experienced Kubernetes (K8S) / HPC Engineer to design, deploy, and manage scalable compute platforms supporting AI/ML, simulation, and data-intensive workloads.

Dayforce

AI Engineer Sr

Canada

Apply About employer

Applications open in a new tab.

Job details

Apply by: Dec 6, 2026
Posting language: English
Posted: Oct 5, 2026

Original job description

Dayforce is a global human capital management (HCM) company headquartered in Toronto, Ontario, and Minneapolis, Minnesota, with operations across North America, Europe, Middle East, Africa (EMEA), and the Asia Pacific Japan (APJ) region.

Our award-winning Cloud HCM platform offers a unified solution database and continuous calculation engine, driving efficiency, productivity and compliance for the global workforce.

Our brand promise - Makes Work Life Better™ - Reflects our commitment to employees, customers, partners and communities globally.

About the opportunity

We are looking for an AI Engineer - Agentic Systems Evaluation to help define how we measure, test, and improve the quality of enterprise AI systems.

As AI evolves from conversational assistants and Retrieval-Augmented Generation (RAG) into tool using agents, multi-agent systems, and autonomous business workflows, evaluating only the final response is no longer enough.

An agent may reach the right answer while choosing the wrong tool, taking unnecessary steps, retrieving incorrect context, failing to escalate to a human, or violating business rules.

This role will build the evaluation frameworks needed to understand not only whether an AI system succeeded, but how it succeeded, how reliably it can repeat that outcome, and whether the architecture is appropriate for the problem.

What you’ll get to do

Build Agentic Evaluation Frameworks

Design evaluation methodologies covering the complete AI execution lifecycle:

Intent → Planning → Retrieval → Tool Use → Reasoning → Action → Business Outcome

Evaluate systems across dimensions including:

task and business outcome accuracy
planning and decision quality
retrieval quality and groundedness
tool selection and execution
agent routing and delegation
human escalation decisions
reliability and failure recovery
safety and policy compliance
latency, token consumption, and cost

Evaluate Agentic Design Patterns

Design experiments and benchmarks that help engineering teams determine which architecture works best for a given problem.

Evaluate patterns such as:

Single-agent vs. multi-agent systems
Supervisor/router architectures
Planner–executor patterns
Sequential and parallel workflows
Tool-using agents
Human-in-the-loop workflows
Long-running agents with state and memory

Measure whether additional agent complexity actually improves task success, reliability, and business outcomes enough to justify increased latency, cost, and operational complexity.

Advance RAG & Knowledge Evaluation

Build rigorous evaluation approaches for enterprise retrieval and knowledge systems, including:

retrieval precision and relevance
groundedness and citation accuracy
source authority and freshness
chunking, metadata, and indexing strategies
semantic vs. hybrid search and reranking
permission-aware retrieval

Evaluate how retrieval decisions ultimately impact downstream agent performance, rather than treating RAG evaluation as an isolated problem.

Build Automated Evaluation & Regression Testing

Develop scalable evaluation infrastructure including:

golden and synthetic datasets
scenario and adversarial test suites
deterministic graders
LLM-as-a-Judge evaluation
human evaluation workflows
trace-based evaluation
automated regression testing

Integrate evaluations into AI development and release pipelines so changes to models, prompts, retrieval, tools, or agent architectures can be measured before reaching production.

Build Agent Trace & Failure Analysis

Analyze complete agent execution traces including planning, retrieved context, tool calls, handoffs, retries, exceptions, latency, and cost.

Develop failure taxonomies that distinguish between:

Turn production failures and user feedback into measurable regression tests and engineering improvements.

Define Production AI Quality

Establish measurable quality standards and release criteria for AI systems.

Metrics may include Task Success Rate, First-Pass Success Rate, Tool Selection Accuracy, Agent Routing Accuracy, Plan Execution Fidelity, Failure Recovery Rate, Human Escalation Accuracy, Business Outcome Accuracy, Cost / Latency per Successful Task

Help teams answer a fundamental question:

Is this AI system reliable, safe, efficient, and valuable enough to operate in production?

Skills and experience we value

Strong experience with:

Python and software engineering
LLM application development
AI/ML evaluation and experimentation
automated testing and data analysis
RAG, embeddings, hybrid search, and reranking
tool/function calling and structured outputs
agent orchestration and multi-agent workflows
AI observability and tracing
APIs and enterprise integrations

Experience with platforms or frameworks such as Agents SDK, LangGraph/LangChain, Microsoft AI Foundry, Amazon Bedrock, or similar agent platforms is valuable.

You should be comfortable working with evaluation techniques such as offline/online evals, deterministic graders, model-based graders, human evaluation, synthetic datasets, adversarial testing.

What would make you stand out

You don't stop when an agent successfully completes a task. You ask:

Did it choose the right approach?
Did it use the right tools and information?
Can it succeed consistently?
Can it recover when something fails?
Did adding more agents improve the outcome?
Could a simpler architecture achieve the same result?
What did the successful outcome cost?

You turn those questions into measurable experiments that help engineering teams build better AI systems.

Why This Role Matters

Enterprise AI is moving from systems that answer questions to systems that make decisions and perform work.

That changes how quality must be measured.

The next generation of AI evaluation must measure:

What the system understood → what it retrieved → what it decided → what it did → and whether the business outcome was correct.

This role will help establish the engineering discipline required to make those systems measurable, reliable, and production-ready.

What’s in it for you

Dayforce is fueled by the diversity of our talented employees. We are an equal opportunity employer and consider and embrace ALL individuals and what makes them unique. We believe our employees should be happy and healthy, with peace of mind and a sense of fulfillment.

We encourage individuals to apply based on their passions.

Dayforce encourages personal and professional growth. We offer excellent time away from work programs, comprehensive wellness initiatives and recognition through competitive pay and benefits.

With a commitment to community impact, including volunteer days and our charity, Dayforce Cares we provide opportunities for you to thrive both in your career and personal life. Our focus is not just on your job but on supporting you to be the best version of yourself.

This job posting is for an existing vacancy

Artificial intelligence may be used in the screening, assessment, or selection of applicants for this position.

About the Salary Ranges

Please note that the salary range mentioned in this job description should serve simply as a guide. The final compensation offered may vary based on a variety of factors, including bonuses and/or incentives, or a candidate’s experience, skills, budget and location. Our company is committed to providing a fair, equitable, and competitive package that reflects the value an individual brings to the organization.

Proficiency in English is required for this position as this role will regularly interact with English-speaking stakeholders, co-workers, managers and/or clients across the world. Further, our back office support teams, including but not limited to Human Resources, are primarily English speaking. Employees need to be able to communicate with these departments in English to appropriately administer their business relationship. Due to the significant high volume of interactions with these English-speaking co-workers, managers, stakeholders and/or clients, which is inherent to this position, it is not possible to reorganize the company's activities to avoid this requirement.

Fraudulent Recruiting

Beware of fraudulent recruiting. Legitimate Dayforce contacts will use an @dayforce.com email address. We do not request money, checks, equipment orders, or sensitive personal data during the recruitment process. If you have been asked for any of the above, or believe you have been contacted by someone posing as a Dayforce employee, please refer to our fraudulent recruiting statement found here: https://www.dayforce.com/be-aware-of-recruiting-fraud

Dayforce actively monitors all job applications to ensure authenticity. Submissions determined to be fraudulent or misleading will be declined from the recruitment process

Pour consulter cette offre d'emploi en français, veuillez utiliser le lien: https://jobs.dayforcehcm.com/fr-CA/mydayforce/alljobs

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More about this job

Responsibilities

You will design and build evaluation frameworks to measure the quality, reliability, and performance of agentic AI systems throughout their execution lifecycle. This involves creating automated regression testing, failure analysis, and benchmarking to ensure AI systems are production-ready.

Requirements

The role requires strong experience in Python, software engineering, and LLM application development, specifically with RAG and agent orchestration. Candidates should be comfortable with evaluation techniques such as offline/online evals, deterministic graders, and synthetic datasets.

Skills

Python
Software engineering
LLM application development
AI/ML evaluation
Automated testing
Data analysis
RAG
Embeddings
Hybrid search
Reranking
Tool calling
Agent orchestration
Multi-agent workflows
AI observability
Tracing
Enterprise integrations

Benefits

Time away from work programs
Wellness initiatives
Competitive pay
Charity programs

Visa sponsorship

Not detected in the job text

Keywords

AI Engineer
Agentic Systems
Python
LLM
RAG
Machine Learning
Evaluation Frameworks
Automated Testing
Data Analysis
Embeddings
Hybrid Search
Reranking
Agent Orchestration
Multi-agent Workflows
AI Observability
Tracing
Enterprise Integrations
LangGraph
LangChain
Microsoft AI Foundry
Amazon Bedrock
Synthetic Datasets
Adversarial Testing
Regression Testing
Human-in-the-loop
Cloud HCM
Software Engineering
API
Failure Analysis
Production AI

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