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Mlops Jobs in Alberta (NOW HIRING)

SMS Equipment

Senior Data & AI Architect

Acheson, AB

Design scalable Data & AI platforms supporting data ingestion, quality management, feature engineering, model development, deployment, monitoring, and continuous improvement through DataOps, MLOps ...

Clio

Senior Platform Engineer

Calgary, AB · Remote

CA$157K - CA$212K/yr

Partner with Security, MLOps, and Product Engineering teams to deliver scalable, resilient, and compliant systems. What you may have: * Deep experience designing, deploying, and operating AWS ...

AltaML

Manager, Software Engineering

Calgary, AB

CA$140K - CA$170K/yr

Familiarity with AI/ML solutions and modern MLOps or software delivery practices. * A track record of championing responsible, ethical AI and inclusive team culture. $140,000 - $170,000 a year This ...

Wawanesa Insurance

Senior Architect - Data & AI

Edmonton, AB · Hybrid

CA$115K - CA$160K/yr

Design and optimize cloud analytics solutions including data lakes, MLOps pipelines, automation, and CI/CD. Experience designing on a modern lakehouse platform (Databricks preferred) using medallion ...

Wawanesa Insurance

Senior Architect - Data & AI

Calgary, AB · Hybrid

CA$115K - CA$160K/yr

Design and optimize cloud analytics solutions including data lakes, MLOps pipelines, automation, and CI/CD. Experience designing on a modern lakehouse platform (Databricks preferred) using medallion ...

Softchoice

Sr. AI Engineer

Calgary, AB · On-site

CA$91K - CA$114K/yr

Experience with CI/CD solutions in the context of MLOps and LLMOps including automation with IaC (e.g., using Terraform). * Comfort partnering directly with non-technical business stakeholders to ...

Clio

Machine Learning Engineer

Calgary, AB · Hybrid

CA$129K - CA$174K/yr

Work in an agile environment with our team of machine learning engineers, MLOps engineering and full stack developers across a variety of projects What you may have: * Hands-on experience in model ...

Clio

Systems Engineer, Production

Calgary, AB · Remote

Partner with Security, MLOps, and Product Engineering teams to deliver scalable, resilient, and compliant systems. * Define agentic patterns to inflect the way we build, deploy, and observe cloud ...

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Mlops information

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

What are the key skills and qualifications needed to thrive as an MLOps engineer?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are popular job titles related to Mlops jobs in Alberta?

For Mlops jobs in Alberta, the most frequently searched job titles are:

What job categories do people searching Mlops jobs in Alberta look for?

The top searched job categories for Mlops jobs in Alberta are:

What cities in Alberta are hiring for Mlops jobs?

Cities in Alberta with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Alberta as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Solution Architect - Generative AI (Azure AI Stack)

Lantern

Edmonton, AB • Remote

Full-time

Re-posted 24 days ago

Job description

We are currently looking for an Solution Architect - Generative AI who will be responsible for leading our clients in designing and deploying solutions in MS Azure.
Key Responsibilities
We are seeking a visionary Solution Architect with deep expertise in Generative AI to lead the design and delivery of intelligent solutions using the Microsoft Azure AI stack. You will work closely with our enterprise clients to architect and implement cutting-edge AI applications powered by Azure OpenAI, Azure AI Foundry, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), and AI agents.
This role is ideal for someone passionate about transforming business processes through large language models (LLMs), multi-agent orchestration, and scalable AI infrastructure.
Skills, Knowledge and Expertise

Bachelor's or Master's degree in Computer Science, Engineering, or related field.
5+ years of experience in cloud solution architecture, with a strong focus on generative AI.
Hands-on experience with Azure OpenAI, LangChain, and LangGraph.
Deep understanding of LLM orchestration, agent frameworks, and RAG architectures.
Proficiency in Python and experience with cloud-native development.
Familiarity with Azure services such as Azure AI Foundry, Azure Functions, Azure AI Search, Azure Storage, and containers in Azure.
Strong communication and stakeholder engagement skills.

Preferred Skills

Experience with Azure AI Foundry or similar enterprise LLM lifecycle platforms.
Microsoft certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert).
Experience with MLOps, CI/CD for AI workloads, and responsible AI practices.
Familiarity with Semantic Kernel, or other orchestration frameworks.

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