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Ml Platform Engineer Jobs in Alberta (NOW HIRING)

Lead the design and operation of AI/ML platform infrastructure, including model serving and ... Partner with engineering, product, and leadership to align platform strategy with business and ...

Execute ML/AI engineering tasks including exploratory data analysis, data preparation, model ... Develop and optimize AI and GenAI solutions using state-of-the-art tools and platform (AI Foundry ...

The platform is being built from the ground up, and every technical decision you make will shape ... Develop and iterate on AI/ML components -- including LLM-based agents, embedding models, and ...

Working with our data science team to integrate ML/AI models into the platform. * Integrating ... Engineering or Computer Science. * Development experience utilizing Java, Kotlin, SQL, Python ...

... and programming languages (SQL, Oracle, Hadoop, NoSQL) for data manipulation and integration. • Experience with cloud ML platforms (AWS, GCP, Azure) is an asset. • Experience with natural ...

Position Overview We are looking for an experienced and versatile Data Engineer to join our dynamic ... Experience working with AI-driven platforms, data infrastructure supporting AI/ML systems, or ...

Sr. AI Engineer

Calgary, AB · On-site

CA$91K - CA$114K/yr

... AI platform. * Create innovative solutions utilizing AI and LLM technologies to solve complex ... Strong proficiency in Python, with experience developing backend services, APIs, AI/ML applications

Demonstrate Databricks capabilities across Data Engineering, Data Science, ML, and Generative AI ... Stay current on Databricks platform releases and evolving data and AI patterns. * Represent BIG at ...

Senior Data Scientist

Calgary, AB · On-site

CA$90K - CA$160K/yr

The Collection Platforms & AI team you will work on building ML powered products and capabilities ... Linear programming and optimization. * Multi-dimensional optimizers, such as Adam, SGD, Gradient ...

Senior Data Scientist

Calgary, AB · On-site

CA$90K - CA$160K/yr

The Collection Platforms & AI team you will work on building ML powered products and capabilities ... Linear programming and optimization. * Multi-dimensional optimizers, such as Adam, SGD, Gradient ...

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Ml Platform Engineer information

What is an ML Platform Engineer?

ML Platform Engineers are specialized software engineers who design, build, and maintain the infrastructure and tools needed to support the development, deployment, and scaling of machine learning models. They bridge the gap between data science and production engineering by automating model training, monitoring, versioning, and serving. Their work enables data scientists to focus on modeling while ensuring that ML solutions are reliable, reproducible, and scalable in real-world environments.

What skills and qualifications are needed to thrive as an ML Platform Engineer?

To thrive as an ML Platform Engineer, you need a strong background in computer science, software engineering, and machine learning concepts, often supported by a degree in a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), containerization (Docker, Kubernetes), CI/CD pipelines, and knowledge of ML frameworks (TensorFlow, PyTorch) are commonly required. Collaboration, problem-solving, and strong communication skills help you work efficiently with data scientists, engineers, and stakeholders. These skills ensure the development, scalability, and reliability of robust ML infrastructure that empowers teams to deploy and manage models effectively.

How does an ML Platform Engineer typically collaborate with data scientists and software engineers within a company?

ML Platform Engineers work closely with both data scientists and software engineers to streamline the process of developing, deploying, and maintaining machine learning models. They provide the infrastructure and tools necessary for data scientists to build and experiment with models efficiently, while ensuring seamless integration with production systems managed by software engineers. Regular communication, participation in cross-functional meetings, and shared project management tools are common ways teams collaborate. This close collaboration helps to bridge the gap between research and production, ensuring robust, scalable, and reliable ML solutions.

What is the difference between Ml Platform Engineer vs Data Scientist?

AspectML Platform EngineerData Scientist
Required credentialsBachelor's/Master's in CS, Engineering, or related; experience with cloud platformsBachelor's/Master's in Statistics, Math, or CS; strong programming skills
Work environmentBuilds and maintains ML infrastructure, collaborates with engineering teamsAnalyzes data, develops models, and interprets results
Industry usageTech companies, AI startups, enterprises deploying ML systemsResearch institutions, tech firms, data-driven organizations

ML Platform Engineers focus on developing and maintaining the infrastructure that supports machine learning models, while Data Scientists primarily analyze data and build models. Both roles often collaborate but serve different functions within the AI and data ecosystem.

What are popular job titles related to Ml Platform Engineer jobs in Alberta?

For Ml Platform Engineer jobs in Alberta, the most frequently searched job titles are:

Infographic showing various Ml Platform Engineer job openings in Alberta as of August 2026, with employment types broken down into 55% Full Time, 42% Part Time, and 3% Contract. Highlights an 77% Physical, 3% Hybrid, and 20% Remote job distribution.

Staff Platform Engineer

Robots and Pencils

Calgary, AB • On-site, Remote

Contractor

Posted 9 days ago


Job description

Staff Platform Engineer 

Location: This position can be located in the following area(s): remote in Canada

This is a 4 month contract assignment with potential to extend

Company Overview 

Robots & Pencils is an applied AI engineering firm building the next frontier of business architecture. We design and ship AI co-workers that integrate into enterprise operations and deliver measurable results for our clients. We're all in on AWS, combining deep UX capability with senior engineering talent to get AI into production fast and keep it there. 
We've earned the trust of leaders across Consumer Products and Retail, Education, Energy, Financial Services, Healthcare, and Manufacturing and more, and earned a reputation as the nimble alternative to traditional global systems integrators. Founded in 2009, with delivery centers in Canada, the United States, Eastern Europe, and Latin America, we are smaller, faster, and more senior by design. Our teams average 15+ years of experience. We move fast, sweat the details, and build things that actually ship. 

Position Overview 

We're looking for a Staff Platform Engineer to define and lead platform engineering strategy across complex, multi-environment cloud systems. This role is ideal for an experienced engineer who can own infrastructure architecture end-to-end, drive DevSecOps and compliance practices, and serve as a technical leader on the engagements they support. 

In this role, you will work as a key technical contributor on a cross-functional team, defining standards and owning platform reliability, performance, and security at scale. You'll mentor engineers, partner with leadership on infrastructure direction, and lead complex migrations and modernization initiatives. 

Why This Role Matters 

At Robots & Pencils, we design AI systems for a human world. Our name says it all. Robots and pencils means engineering paired with creativity, because every agent we ship has to work for real people in real workflows. That balance is baked into how we operate. 
Every role here contributes directly to that mission. Here, you shape how AI systems integrate into enterprise operations, how teams move at real velocity, and how products create measurable impact for clients and the people they serve. We ship production-ready AI in 30 to 45 days. That pace demands people who take ownership, lead with craft, and care deeply about what they put their name on. 

What You'll Do 

Craft & Delivery 

  • Define DevOps strategy and lead infrastructure architecture across multi-environment, multi-region cloud systems
  • Architect and own scalable Kubernetes platforms and containerized infrastructure at scale
  • Own infrastructure as code strategy and standards across environments
  • Lead DevSecOps implementation including secrets management, compliance, auditing, IAM, and zero-trust networking
  • Drive platform reliability, performance SLAs, and cost optimization across production systems
  • Lead complex cloud migrations and platform modernization initiatives
  • Own observability strategy and production reliability practices
  • Lead the design and operation of AI/ML platform infrastructure, including model serving and deployment, GPU workload orchestration, LLM gateway and observability, vector store infrastructure, and CI/CD for AI/ML systems
  • Bring an AI-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace

Collaboration & Communication 

  • Partner with engineering, product, and leadership to align platform strategy with business and delivery goals
  • Communicate complex infrastructure decisions and tradeoffs clearly to technical and non-technical stakeholders
  • Lead design reviews, architecture discussions, and release readiness assessments

Leadership & Influence 

  • Establish platform engineering standards and best practices on the engagements you support
  • Mentor junior and mid-level engineers, helping them grow their craft, confidence, and impact
  • Act as a technical escalation point on complex infrastructure and platform challenges
  • Evaluate emerging tools and technologies, recommending patterns that improve platform reliability and developer experience

What You'll Bring 

  • 7+ years of professional DevOps or platform engineering experience, with experience leading complex platform initiatives
  • Expert scripting and programming skills (e.g., Python, Go, Java, Bash)
  • Deep cloud expertise across at least one major platform
  • Expert Kubernetes and container orchestration skills
  • Expert IaC skills across multiple tools
  • Strong CI/CD architecture experience at scale
  • Strong DevSecOps experience including secrets management, compliance, and auditing
  • Experience with networking, IAM, security architecture, and zero-trust principles in cloud environments
  • Experience with service mesh, distributed systems, and microservices architecture
  • Strong experience with AI/ML platform infrastructure, including model serving and deployment, GPU workload orchestration, LLM gateway and observability, vector store infrastructure, and CI/CD for AI/ML systems
  • Demonstrated leadership and technical mentoring experience across a team or organization
  • Strong stakeholder communication skills, with the ability to translate technical depth across audiences
  • Demonstrable, day-to-day usage and expert knowledge of AI-forward tools such as Claude and Cursor
  • Excellent problem-solving skills and the ability to navigate highly ambiguous technical and business challenges with sound judgment
  • Cloud certifications (e.g., AWS DevOps Engineer Professional, CKA, Azure DevOps Engineer) or FinOps experience is a plus

Helpful Extras and Unique Skills

  • Designing and provision HPC cluster infrastructure using CI/CD pipeline across AWS, CoreWeave, GCP, and OCI
  • Experience with HPC job schedulers and workload managers such as Slurm or equivalent for job submission and queue management

You'll Do Well Here if You Are 

  • A doer. You see something broken and fix it. You'd rather move on clarity than wait for certainty.
  • A fast learner who knows you don't know everything. The AI landscape changes weekly. You're senior enough to know better and curious enough to keep learning anyway.
  • Direct in a way that makes the work better. You give honest feedback. You'd rather have the hard conversation than blow smoke.
  • Obsessed with craft. You know genius is in the details. You ship exceptional, not perfect, and you don't put your name on work you wouldn't stand behind.
  • Built for ownership. You honor commitments, admit mistakes fast, and back your teammates when a decision costs something. No handoffs, no finger-pointing.
  • All in. You treat clients' businesses like your own. You take the work seriously without taking yourself seriously.
  • Resourceful when the budget, timeline, or team is tight. Constraints don't slow you down. They sharpen you.
  • Glad to be in the room with people who care as much as you do. Our teams average fifteen-plus years of experience. We hire people who push each other to do better work.