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Added 1 month ago
Technical Architect
Mphasis
Toronto, ON
Full-time
We are looking for an experienced MLOPs / LLMOPs Engineer with a strong background in deploying and monitoring machine learning and large language model (LLM) pipelines. The ideal candidate…
Added 11 days ago
Director, Enterprise Machine Learning Frameworks & Operations
CIBC
Toronto, ON
Full-time
…development, and scaling of enterprise-wide ML Operations (MLOps / LLMOps) capabilities. This role is pivotal in establishing AI at scale by driving operational efficiency, reliability, and…
Added 1 month ago
Senior AI Lead -Snowflake
Accenture
Remote, Canada
Full-time · Remote
Operate MLOps / LLMOps pipelines with CI/CD across the ML and LLM lifecycle.
Added 1 month ago
Senior AI Lead: Databricks
Accenture
Remote, Canada
Full-time · Remote
Operate MLOps / LLMOps pipelines with CI/CD across the ML and LLM lifecycle.
Added 2 months ago
Architecte en IA agentique - Intelligence Engineering / Agentic AI - Sr. Architect - Intelligence Engineering
Slalom
Montreal, Quebec
Full-time
Recommander et mettre en œuvre des architectures pour les pipelines de modèles et d’agents, CI/CD, les tests, le déploiement, l’observabilité et les pratiques MLOps et LLMOps à grande…
Added 14 days ago
Account Executive - Greenfield - Toronto
Weights & Biases
Toronto, ON
Full-time
Background selling AI/ML, MLOps, LLMOps, or adjacent developer tooling alongside infrastructure products
Added 15 days ago
Chief Artificial Intelligence Architect
National Bank of Canada
Montreal, QC
Full-time
Solid knowledge of data platforms, MLOps / LLMOps, and cloud-native AI services such as AWS and Azure.
Added 17 days ago
AI and Data - Manager - AI Engineer
EY
Toronto, ON
Implement best practices for MLOps, LLMOps, across experimental and production environments, including AI pipeline and microservices deployment into cloud environments, APIs, CI/CD,…
Added 1 month ago
AI/ML Architect-Accenture Advanced Technology Centers
Accenture
Mississauga, ON · Montreal, QC
Full-time
Run MLOps / LLMOps pipelines for model training, deployment, monitoring, and lifecycle.
Added 1 month ago
Lead AI Security Automation Engineer
State Street
Toronto, ON
Full-time
Lead the architecture and hands-on delivery of scalable, reliable agentic AI platforms for security workflows
Added 10 days ago
Senior AI Security Automation Engineer
State Street
Toronto, ON
Full-time
Drive the architecture and hands-on delivery of scalable, reliable agentic AI platforms for security workflows
Added 10 days ago
Account Executive - Greenfield - Toronto
CoreWeave
Toronto, ON
Background selling AI/ML, MLOps, LLMOps, or adjacent developer tooling alongside infrastructure products
Added 5 days ago
Architect - Machine Learning
Quantiphi
Canada
Full-time
Define architecture standards and reusable patterns for GenAI, ML, MLOps, and LLMOps.
Added 11 days ago
Staff Software Engineer
kadence
Toronto, ON
Full-time
Champion MLOps and LLMOps practices across the development lifecycle
Added 3 days ago
Presales Solutions Architect
Centrilogic
Toronto, ON
Full-time
Presales Solution Architect, Cloud, AI and Data
Added 14 days ago
Account Solution Architect
CoreWeave
Toronto, ON
Full-time
Background in ML Engineering, AI Engineering, MLOps, or LLMOps
Added 25 days ago
Senior Data & AI Architect
SMS Equipment Inc.
Acheson, AB
Full-time
…deployment, monitoring, and continuous improvement through DataOps, MLOps, and…
Added 27 days ago
Sr Director, Enterprise Data and AI Platform
Dayforce
Canada
Establish enterprise MLOps and LLMOps practices for machine learning and generative AI, including model lifecycle management, prompt and model evaluation, monitoring, observability,…
Added 8 days ago
Senior Data Scientist, Commercial
HelloFresh
Toronto, ON
Contribute to the development, implementation, and governance of the global MLOps and LLMOps platforms in Canada.
Added 1 month ago
Sr. AI Engineer
Softchoice
NL · Toronto, ON · Ottawa, ON · Oakville, ON · Calgary, AB · NB · NS · AB · BC · British Columbia, Brit · Nova Scotia, Nova
Experience with CI/CD solutions in the context of MLOps and LLMOps including automation with IaC (e.g., using Terraform).
Added 19 days ago
Account Solution Architect
CoreWeave
Toronto, ON
Background in ML Engineering, AI Engineering, MLOps, or LLMOps
Added 3 months ago
Principal Enterprise Architect, AI Platform
Priceline
Toronto, Ontario
Full-time
Modernize Model Lifecycles: Standardize LLMOps and MLOps patterns using model gateways and automated pipelines (Vertex AI, Composer) to ensure governed, self-service access.
Added 11 days ago
Principal Architect - AI and Data Platforms
Canadian Tire Corporation
Calgary, AB · Mississauga, ON · Oakville, ON · Toronto, ON
Full-time
As a Principal Architect – AI & Data Platforms, you will play a pivotal leadership role within the Enterprise AI and Emerging Technology team, responsible for shaping and executing the…
Added 1 month ago
AI Engineer
Guidepoint
Toronto, ON
…ensuring the integration of responsible AI and best-in-class MLOps. The AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other…
Added 1 month ago
Senior Data/AI Engineer
Guidepoint
Toronto, ON
…ensuring the integration of responsible AI and best-in-class MLOps. The Senior AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and…

Guidepoint

AI Engineer

Toronto, ON, Canada

Experience: Production AI Application Experience: 2+ years of experience building applications that leverage large language models from providers like OpenAI, Anthropic, or Google Gemini.
Posting language: English
Posted: Aug 18, 2026

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Original job description

Overview:

Guidepoint seeks an experienced AI Engineer as an integral member of the Toronto-based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting-edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next-Gen research enablement platform and data products.

This role demands exceptional leadership and technical prowess to drive the development of next-generation research enablement platforms and AI-driven data products. You will develop and scale Generative AI-powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best-in-class MLOps. The AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state-of-the-art tools across all of Guidepoint’s products.

Guidepoint’s Technology team thrives on problem-solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge-sharing solutions, the engineering team is taking on challenges to improve our internal application architecture and create new AI-enabled products to optimize the seamless delivery of our services.

This is a hybrid position based in Toronto.

What You'll Do:

Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems.
Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization in production environments like Databricks and Azure Kubernetes Service (AKS).
Optimize RAG Pipelines: Continuously improve retrieval and generation quality through techniques like retrieval optimization (tuning k-values, chunk sizes), using re-rankers, advanced chunking strategies, and prompt engineering for hallucination reduction.
Integrate Intelligent Systems: Engineer solutions that seamlessly combine LLMs with our proprietary knowledge repositories, external APIs, and real-time data streams to create powerful copilots and research assistants.
Champion LLMOps and Engineering Best Practices: Collaborate with data science and engineering teams to establish and implement best practices for LLMOps, including automated evaluation using frameworks like LLM Judges or MLflow, AI observability, and system monitoring.
Evaluate and Implement AI Strategies: Systematically evaluate and apply advanced prompt engineering methods (e.g., Chain-of-Thought, ReAct) and other model interaction techniques to optimize the performance and safety of proprietary and open-source LLMs.
Mentor and Lead: Provide technical leadership to junior engineers through rigorous code reviews, mentorship, and design discussions, helping to elevate the team's engineering standards.
Influence the Roadmap: Partner closely with product and business stakeholders to translate user needs into technical requirements, define priorities, and shape the future of our AI product offerings.

What You Have:

Experience: A Bachelor’s degree in Computer Science, Engineering, or a related technical field with 6+ years of professional experience; or a Master’s degree with 4+ years of professional experience in backend software engineering and Generative AI. This must include a proven track record of designing, building, and scaling distributed, production-grade systems.
Strong Software Engineering Fundamentals: Deep expertise in Python, a major backend framework (e.g., FastAPI, Flask), and asynchronous programming (e.g., asyncio). Proficiency in designing RESTful APIs, microservices, and the complete operational lifecycle, including comprehensive testing, CI/CD (e.g., ArgoCD), observability, monitoring, alerting, maintaining high uptime, and executing zero-downtime deployments.
Cloud & Infrastructure Proficiency: Hands-on experience deploying and managing applications on a major cloud platform (Azure preferred, AWS/GCP acceptable) using containerization (Docker) and orchestration (Kubernetes, Helm).
Production AI Application Experience: 2+ years of experience building applications that leverage large language models from providers like OpenAI, Anthropic, or Google Gemini. Direct experience with modern LLM patterns such as retrieval-augmented generation (RAG), hybrid search using vector databases (e.g., Pinecone, Elasticsearch), multi-agent AI systems with tool calls, and prompt engineering is required.
AI System Design and Evaluation: Experience designing and implementing robust evaluation frameworks for LLM-based systems, including rubric-based scoring, LLM Judges, or using tools like MLflow, alongside monitoring for performance and drift.
Large-Scale Data Processing: Familiarity with large-scale data processing platforms and tools (e.g., Databricks, Apache Spark).
Familiarity with the Modern AI Stack: Practical experience with libraries and frameworks like LangChain or LlamaIndex for building LLM-powered applications.
Leadership and Mentorship: Demonstrated ability to lead complex technical projects and foster the growth of other engineers.

What We Offer:

The annual base salary range for this position is $135,000 - $210,000. Additionally, this position is eligible for an annual discretionary bonus based on performance.

You will also be eligible for the following benefits:

Paid Time Off
Comprehensive benefits plan
Company RRSP Match
Development opportunities through the LinkedIn Learning platform

Guidepoint's Interview Process:

We respect your time and aim to keep the process transparent and efficient. Our Talent Acquisition team members act as advisors throughout the process and help ensure everything runs smoothly so you have a seamless candidate experience. Our goal is to make sure this is a mutual fit for both you and our teams.

Talent Acquisition Screen (Virtual, 30 minutes)
Hiring Manager Interview (Virtual, 1 hour)
Live Coding & System Design Test (On-site, 2 hours)
Technical Leadership Interview (Virtual, 1 hour)
Reference Checks (3 professional references)
Offer

About Guidepoint:

Guidepoint powers end-to-end research workflows for the world’s best research teams.

Backed by a global network of more than 2 million experts and over 1,600 employees, Guidepoint delivers real-time access to expertise, primary research, and actionable knowledge that help organizations make informed decisions. Through consultations, surveys, events, proprietary content, and AI-enabled tools, we support every stage of the decision-making process - from developing hypotheses and gathering insights to validating assumptions and building conviction for critical decisions.

At Guidepoint, our success relies on the diversity of our employees, experts, and clients, which enables us to foster meaningful connections and a broad range of perspectives. We are committed to creating an inclusive and welcoming environment where individuals of all backgrounds, identities, and experiences can contribute and succeed.

This job post is for a live vacancy. Base salary may vary depending on job-related knowledge, skills, and experience, as well as geographic location.

Compensation

$135,000 - $210,000 CAD

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More about this job

Responsibilities

Architect and build scalable production systems and APIs to serve Generative AI features and RAG pipelines. Lead the end-to-end AI application lifecycle, including deployment, monitoring, and the implementation of LLMOps best practices.

Requirements

Requires a Bachelor's or Master's degree with 4-6+ years of experience in backend software engineering and Generative AI. Must have proven expertise in Python, cloud infrastructure (Azure preferred), and building production-grade LLM applications.

Skills

Python
Generative AI
LLM
RAG
FastAPI
Kubernetes
Azure
Databricks
MLOps
PyTorch
LangChain
LlamaIndex
Vector Databases
System Design
CI/CD
Asynchronous Programming

Benefits

Paid Time Off
Comprehensive Benefits Plan
Company RRSP Match
Development Opportunities Through LinkedIn Learning

Visa sponsorship

Not detected in the job text

Keywords

Generative AI
Large Language Models
LLM
RAG
Retrieval-Augmented Generation
Databricks
Azure
Kubernetes
Docker
FastAPI
Flask
Asyncio
MLflow
LangChain
LlamaIndex
Pinecone
Elasticsearch
Apache Spark
CI/CD
ArgoCD
LLMOps
Prompt Engineering
Chain-of-Thought
ReAct
Microservices
RESTful APIs
Vector Databases
AI Observability
System Design
Distributed Systems

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