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Added 22 days ago
R
AI Architect (T & I) (Telework/Hybrid)
Radio-Canada
Montreal, QC
- Full-time
Develop robust MLOps/LLMOps pipelines with a constant focus on observability, performance profiling and automated testing.
Added 25 days ago
L
Data Scientist
Lantern
AB · Edmonton, AB
- Full-time
Implement MLOps and LLMOps practices including source control, automated testing, CI/CD, model registration, deployment, monitoring, drift detection, responsible AI, and cost optimization.
Added 25 days ago
Data Scientist
Lantern
Edmonton, AB
Implement MLOps and LLMOps practices including source control, automated testing, CI/CD, model registration, deployment, monitoring, drift detection, responsible AI, and cost optimization.
Added 25 days ago
RB
Principal AI Engineer
Royal Bank of Canada
Calgary, AB
- Full-time
MLOps and LLMOps experience, including CI/CD for ML and LLM applications, model registries, feature stores, experiment tracking, and safe rollout patterns
Added 27 days ago
C
Senior Agentic AI Developer
CGI
Toronto, ON
- Full-time
Understanding of MLOps/LLMOps practices, CI/CD pipelines, model monitoring, and observability.
Added 28 days ago
R
Principal AI Engineer
RBC
Calgary, AB
- Full-time
MLOps and LLMOps experience, including CI/CD for ML and LLM applications, model registries, feature stores, experiment tracking, and safe rollout patterns
Added 1 month ago
UR
AI Platform Engineering Lead
Urban Ridge Supplies
Toronto, ON
- Full-time
…technical leadership responsibility, intended for an engineer-architect who is energized by building a new capability from a blank page and who is comfortable making decisions with…
Added 1 month ago
UR
Staff Software Engineer / Architect - AI, CoCounsel FDE
Urban Ridge Supplies
Toronto, ON
- Full-time
Establish appropriate observability, monitoring, evaluation, MLOps/LLMOps, security, and governance practices.
Added 1 month ago
W&
Account Executive - Greenfield
Weights & Biases
Toronto, ON
- Full-time
Background selling AI/ML, MLOps, LLMOps, or adjacent developer tooling alongside infrastructure products
Added 1 month ago
AI
AI Platform Engineering Lead
AGF Investments
Toronto, ON
- Full-time
…technical leadership responsibility, intended for an engineer-architect who is energized by building a new capability from a blank page and who is comfortable making decisions with…
Added 1 month ago
TC
Solutions Architect
Tata Consultancy Services
Mississauga, ON
- Full-time
Establish LLMOps/MLOps and CI/CD processes for automated testing, deployment and rollback.
Added 1 month ago
AM
AI Platform Engineering Lead
AGF Management
Toronto, ON
- Full-time
…technical leadership responsibility, intended for an engineer-architect who is energized by building a new capability from a blank page and who is comfortable making decisions with…
Added 1 month ago
C
Presales Solutions Architect
Centrilogic
Toronto, ON
- Full-time
Presales Solution Architect, Cloud, AI and Data
Added 1 month ago
Q
Architect - Machine Learning
Quantiphi
Canada
- Full-time
Experience designing and implementing MLOps/LLMOps pipelines, including model deployment, monitoring, evaluation, versioning, and CI/CD.
Added 1 month ago
K
Staff Software Engineer
kadence
Toronto, ON
- Full-time
Champion MLOps and LLMOps practices across the development lifecycle
Added 1 month ago
W&
Account Executive - Greenfield - Toronto
Weights & Biases
Toronto, ON
- Full-time
Background selling AI/ML, MLOps, LLMOps, or adjacent developer tooling alongside infrastructure products
Added 1 month ago
UC
Lead Full Stack Developer
UniSysTech Consulting
Toronto, ON · Greater Toronto Area, Canada
- Other
CI/CD & MLOps/LLMOps: Establish and refine CI/CD automated deployment pipelines using Infrastructure-as-Code (Terraform, AWS CDK) and support model evaluation/monitoring deployment…
Added 2 months ago
M
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 2 months ago
B
Principal Architect, AI Platform
BMO
Toronto, ON · BMOPLACE
- Full-time
…architectural authority. We are seeking a deeply experienced architect to own architectural coherence for AI across BMO's cloud and workspace estates - spanning AWS, Azure, Microsoft 365,…
Added 2 months 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 2 months ago
Senior Manager AI Solutions Architecture
Diligent Corporation
Vancouver, BC
Familiarity with MLOps/LLMOps practices (model lifecycle, experiment tracking, evaluation, drift monitoring) and modern engineering practices such as containerization and CI/CD.
Added 2 months ago
A
Senior Principal Engineer
Autodesk
Toronto, ON · AMER - Canada - Ontario - Toronto - University Ave
- Full-time
…or vLLM, vector-search such as OpenSearch or pgvector, and MLOps/LLMOps practices for evaluation, tracing, versioning, deployment, monitoring, and governance, or equivalent technologies…
Added 1 month 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 2 months ago
A
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 3 months ago
S
Architecte en IA agentique - Intelligence Engineering / Agentic AI - Sr. Architect - Intelligence Engineering
Slalom
Montreal, Quebec
- Full-time
…les tests, le déploiement, l’observabilité et les pratiques MLOps et LLMOps à grande…

Quantiphi

Architect - Machine Learning

Canada

Full-time

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Job details

Job type: Full-time
Posting language: English
Posted: Sep 14, 2026

Original job description

About Quantiphi:

Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies - we solve the problems that matter most to our clients.

Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.

Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters

We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:

21 Google Cloud Partner of the Year awards in the past 10 years
3 AWS AI/ML Partner of the Year awards
3 NVIDIA Partner of the Year awards
3 Snowflake Partner of the Year awards
Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms

Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators.

We are also proud to be certified as a Great Place to Work - reflecting our commitment to our people and our culture.

For more details, visit: Website or LinkedIn Page

Job Description:

Required Skills:

8+ years of experience in Machine Learning, AI, Software Engineering, Data Science, or Solution Architecture.
Strong experience designing and implementing enterprise-scale AI/ML architectures.
3+ years of hands-on experience with Generative AI, LLMs, and production AI applications.
Strong expertise in RAG (Retrieval-Augmented Generation), embeddings, vector databases, semantic/hybrid search, and knowledge retrieval.
Hands-on experience with Agentic AI, AI agents, tool/function calling, orchestration, and multi-agent workflows.
Strong proficiency in Python and experience with ML/AI frameworks such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
Experience with LLM platforms/models such as Azure OpenAI, OpenAI, AWS Bedrock, Anthropic, Google Gemini, Llama, or equivalent.
Strong knowledge of prompt engineering, context engineering, fine-tuning, model evaluation, guardrails, and hallucination mitigation.
Experience designing and implementing MLOps/LLMOps pipelines, including model deployment, monitoring, evaluation, versioning, and CI/CD.
Strong experience with at least one major cloud platform: Microsoft Azure, AWS, or GCP.
Experience with cloud AI/ML services such as Azure Machine Learning, Azure AI Foundry, AWS SageMaker/Bedrock, or Google Vertex AI.
Experience with Docker, Kubernetes, APIs, microservices, CI/CD, and Infrastructure as Code.
Strong understanding of data pipelines, data lakes/lakehouses, databases, data governance, and enterprise integration.
Experience with AI/ML security, privacy, Responsible AI, governance, and compliance.
Strong system-design and architecture skills, with the ability to translate business requirements into scalable technical solutions.
Excellent communication, presentation, stakeholder-management, and technical leadership skills.

Key Responsibilities:

Design end-to-end enterprise AI/ML architectures from data ingestion through model development, deployment, inference, monitoring, and optimization.
Lead the architecture and implementation of Generative AI and LLM-based solutions across enterprise use cases.
Design scalable RAG architectures, including document ingestion, chunking, embeddings, vector search, retrieval, reranking, and LLM generation.
Architect and implement Agentic AI solutions, including AI agents, tool calling, workflow orchestration, memory, and human-in-the-loop capabilities.
Evaluate and recommend LLMs, AI platforms, frameworks, and technologies based on performance, scalability, security, latency, and cost.
Define architecture standards and reusable patterns for GenAI, ML, MLOps, and LLMOps.
Lead the transition of AI/ML POCs and prototypes into production-grade solutions.
Design and implement MLOps/LLMOps capabilities for model lifecycle management, deployment, monitoring, evaluation, and continuous improvement.
Establish LLM evaluation and observability frameworks covering accuracy, relevance, hallucination, latency, token usage, cost, and reliability.
Work closely with Data Scientists, ML Engineers, Software Engineers, Data Engineers, Product Managers, Security teams, and business stakeholders.
Provide technical leadership and mentorship to AI/ML engineering teams.
Ensure AI solutions meet enterprise requirements for security, privacy, governance, Responsible AI, and regulatory compliance.
Identify opportunities to improve existing AI/ML platforms and optimize performance, scalability, and cloud costs.
Create architecture diagrams, technical specifications, design documents, standards, and implementation roadmaps.
Stay current with emerging GenAI, LLM, Agentic AI, ML, and cloud technologies and assess their applicability to business needs.

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More about this job

Responsibilities

Design and implement end-to-end enterprise AI/ML architectures, focusing on Generative AI, RAG, and Agentic AI solutions. Lead the transition of prototypes into production-grade solutions while establishing evaluation and observability frameworks.

Requirements

Requires 8+ years of experience in AI/ML or Software Engineering, with at least 3 years specifically in Generative AI and LLMs. Proficiency in Python, major cloud platforms (Azure, AWS, or GCP), and MLOps pipelines is essential.

Skills

Machine Learning
Generative AI
LLMs
RAG
Agentic AI
Python
PyTorch
TensorFlow
MLOps
LLMOps
Azure OpenAI
AWS Bedrock
Google Vertex AI
Docker
Kubernetes
System Design

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Artificial Intelligence
Generative AI
Large Language Models
RAG
Vector Databases
Agentic AI
Python
PyTorch
TensorFlow
Hugging Face
Azure OpenAI
AWS Bedrock
Google Gemini
Llama
MLOps
LLMOps
CI/CD
Docker
Kubernetes
Microservices
Infrastructure as Code
Data Lakes
Responsible AI
System Design
Cloud Computing
Azure
AWS
GCP
Vertex AI

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