1

Ai Integration Engineer Jobs in Calgary, AB (NOW HIRING)

Developer Advocate, AI Enablement

Calgary, AB ยท On-site +1

CA$93K - CA$141K/yr

Your Opportunity The Developer Advocate will serve as a critical bridge between BDO Canada's AI ... Partner with IT to integrate AI development tools securely and efficiently into BDO Canada ...

Whether you're optimizing energy efficiency, integrating resilient systems, or engineering the ... Experience using AI productivity tools to accelerate project workstreams including technical ...

Work closely with designers, developers, data engineers, and integration architects to bring AI-driven experiences to life. Education: * Bachelor's degree or equivalent work experience required.

Design plays a pivotal role at Clio, standing alongside Product and Engineering as one of the three ... AI Integration: Explore how Clio's AI capabilities integrate with document workflows as they expand ...

New

... engineering, data, and IT/OT teams to understand system constraints and integration paths ... Evaluate emerging AI platforms, frameworks, and tools for practical fit within client environments ...

Knowledge of the TC Energy integrated pipeline systems would be an asset * Pipeline hydraulic simulation (Synergi Gas) experience is considered an asset * Programming (e.g., Python, VBA, PowerBI), AI ...

... AI applications and projects, including API design, data models, and integration patterns ... Proven expertise in software engineering practices, including code review, testing, design patterns ...

Showing results 21-40

Ai Integration Engineer information

What is an AI integration engineer?

AI Integration Engineers are professionals who specialize in implementing artificial intelligence solutions into existing systems, products, or workflows. They work closely with data scientists, software developers, and business teams to ensure that AI models and technologies are effectively deployed and seamlessly integrated. Their responsibilities often include customizing AI tools, developing APIs, ensuring data compatibility, and monitoring performance post-integration. These engineers play a crucial role in bridging the gap between AI research and practical business applications.

What are some common challenges faced by AI integration engineers when deploying machine learning models into existing business systems?

AI Integration Engineers often encounter challenges such as ensuring compatibility between machine learning models and legacy systems, managing data privacy and security, and optimizing model performance for real-time applications. They must also address issues related to model scalability and monitoring, as well as facilitate smooth collaboration between data science, IT, and business teams. Overcoming these challenges requires strong problem-solving skills, effective communication, and a deep understanding of both AI technologies and enterprise infrastructure.

What are the key skills and qualifications needed to thrive as an AI integration engineer, and why are they important?

To thrive as an AI Integration Engineer, you need a solid background in computer science, programming (Python, Java, or similar), and experience with AI/ML frameworks, often supported by a bachelor's degree in a related field. Familiarity with cloud platforms (such as AWS, Azure, or Google Cloud), API development, and tools like TensorFlow or PyTorch is typically required. Strong problem-solving abilities, collaboration, and clear communication are essential soft skills for bridging technical and business needs. These competencies ensure successful deployment and seamless integration of AI solutions into existing systems, driving innovation and business value.

What is the difference between Ai Integration Engineer vs Data Scientist?

AspectAi Integration EngineerData Scientist
Required CredentialsBachelor's in CS, Engineering, or related; certifications in AI/ML toolsBachelor's or higher in CS, Statistics, or related; advanced degrees common
Work EnvironmentDeveloping and deploying AI solutions, integrating AI APIs into applicationsAnalyzing data, building predictive models, interpreting complex datasets
Employer & Industry UsageTech companies, AI service providers, software firmsResearch institutions, tech companies, finance, healthcare

While both roles involve AI, the Ai Integration Engineer focuses on implementing and integrating AI solutions into applications, whereas the Data Scientist analyzes data to develop models and insights. The roles often overlap but differ mainly in their primary focus: deployment versus analysis.

Are AI Integration Engineers highly paid?

AI Integration Engineers typically earn higher-than-average salaries due to their specialized skills in AI systems, programming, and data analysis. Compensation varies based on experience, location, and industry, but they are generally well-compensated compared to many other engineering roles.

What are popular job titles related to Ai Integration Engineer jobs in Calgary, AB?

For Ai Integration Engineer jobs in Calgary, AB, the most frequently searched job titles are:

What job categories do people searching Ai Integration Engineer jobs in Calgary, AB look for?

The top searched job categories for Ai Integration Engineer jobs in Calgary, AB are:

Infographic showing various Ai Integration Engineer job openings in Calgary, AB as of August 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution.

Principal Solution Architect, AI Co-innovation

Cognite - AI for Industry

Calgary, AB โ€ข On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 20 days ago


Job description

What Cognite is: Relentless to achieve

Cognite operates at the forefront of industrial digitalization, building AI, and data solutions that solve the world's hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements.

We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you'll feel right at home here. 

Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.


How you'll demonstrate OwnershipThe Team

The AI Co-Innovation team operates at the intersection of Cognite's emerging product capabilities and our most forward-leaning customers. We work closely with product teams to operationalize new AI capabilities—hardening, scaling, and integrating them into production environments where they deliver real-world impact.

We drive transformational customer engagements, product improvements, and measurable outcomes. We take ideas from prototype to production, ensuring they're reliable, scalable, and ready for repeatable delivery. Speed is central to how we work.

The team combines full-stack engineers, deployed AI engineers, and technical program managers working toward shared objectives.

The Role

As an Enterprise Solution Architect, you will be a high-leverage technical leader responsible for the architectural integrity and strategic success of our AI deployments. You will own the data model quality and architectural standards for Cognite AI Agents and Custom Front-end Applications, ensuring that complex industrial data is structured and contextualized to power world-class agentic systems.

This role sits at the center of our scaling strategy. You will act as the bridge between product engineering, domain experts, and delivery teams, translating high-level business problems into robust, scalable technical architectures. You are not just designing systems; you are establishing the repeatable patterns that will allow Cognite to transform the industrial world.

The Impact you bring to CogniteWhat You'll Do

Own Architectural Excellence: Set and maintain the standards for data model quality and system architecture across strategic accounts to ensure reliable, production-grade AI solutions.

Architect Complex Data Models: Design and implement sophisticated industrial data models within Cognite Data Fusion (CDF), providing the necessary "ground truth" for LLMs and agentic workflows.

Design agent workflows: Architect multi-agent systems that combine LLMs, tool use, evaluation loops, and reasoning over industrial data.

Execute directly with customers: Collaborate with technical teams on-site to refine use cases and integrate solutions into production environments.

Scale Repeatable Patterns: Identify and document architectural patterns that reduce delivery friction and enable the team to move from bespoke prototypes to repeatable, enterprise-scale impact.

Mentor and Elevate: Raise the technical ceiling of the Co-Innovation team by mentoring more junior engineers in systems thinking and CDF best practices.

Shape the product: Feed learnings and customer feedback into the product roadmap systematically - ensuring we build the most compelling industrial AI platform in the market.

What We're Looking For

Senior Engineering Background: 10+ years of experience in software engineering or solution architecture, with a "builder" mindset and the ability to dive into the code when necessary. Ideally, you have also worked with AI solution architecture, or similar roles - including direct customer-facing work

You've built with AI systems. Hands-on experience with LLMs, embeddings, prompt engineering, and related techniques.

Solution Leadership: You have a proven track record of designing and shipping enterprise-scale software architectures, preferably in an AI or data-heavy context.

You communicate clearly. Strong storytelling skills for both technical and executive audiences.

You write quality code. Clean, maintainable, and scalable - with solid practices around testing, version control, and code review.

You work at the frontier. Familiarity with modern AI development workflows, rapid iteration, and agentic engineering tools as part of your daily workflow.

Nice to Have
  • Understanding of industrial data types (time series, knowledge graphs)
  • Experience with Cognite Data Fusion
  • Familiarity with vector databases and RAG architectures
  • Cloud-native development experience (AWS, Azure, GCP)
  • Exposure to industrial domains - manufacturing, energy, process industries
  • Experience with industrial control systems
  • Deep fluency with agentic coding tools and an understanding of what they mean for the future of software engineering - and industrial workflows
A snapshot of our many perks and benefits as a Cogniter
* Competitive compensation
* 401(k) with employer matching
* Competitive health, dental, vision & disability coverages for employees and all dependents
* Unlimited PTO
* Paid Parental Leave Program
* Employee Referral Program
 
 
 
Learn more about us
 
 
 
  • Impact 2025
  • Cognite's Industrial AI: Moonshot
  • We're globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.
 
 
Equal Opportunity
Cognite is committed to creating a diverse and inclusive environment at work and is proud to be an equal opportunity employer. All qualified applicants will receive the same level of consideration for employment.