AI Intern

About ProCogia: 

ProCogia is a data and AI consulting firm helping organizations turn complex technology challenges into measurable business outcomes. We work across highly regulated and high-stakes industries, including telecommunications, financial services, life sciences, healthcare, and the public sector, where security, governance, reliability, and performance are critical. Our teams bring together expertise across data science, engineering, AI, analytics, and consulting to design and deliver practical, production-ready solutions. We are actively advancing capabilities across generative AI, agentic systems, workflow automation, AI governance, and enterprise AI applications. At ProCogia, you will work alongside experienced practitioners on meaningful technical challenges and contribute directly to both client solutions and the platforms and products we are building internally.

Our Core Values: Trust, Growth, Innovation, Excellence, and Ownership

Work Location: Vancouver (On-site)  (Full-Time Work from office in Vancouver, five days a week Mon-Fri)  

Job Summary 

We are seeking AI Interns (Co-op Students) to join our team from December 2026 – April 2027. This is a hands-on role for students who want to go beyond AI experimentation and learn what it takes to build AI systems that are reliable enough for real enterprise environments. You will work alongside ProCogia’s AI, data science, and engineering teams to design, build, evaluate, and improve AI agents, agentic workflows, RAG systems, and LLM-powered applications. We are looking for curious builders with strong technical fundamentals, an experimentation mindset, and the discipline to test and validate AI outputs rather than trust them blindly.

Key Responsibilities 

• Design, build, test, and improve AI agents and agentic workflows for internal and client-facing solutions.
• Develop RAG pipelines that provide grounded responses with citations and traceable source information.
• Integrate commercial and open-source LLMs into applications using APIs, SDKs, and enterprise systems.
• Apply an evaluation-driven development approach by defining benchmarks, measuring quality, identifying failure modes, and improving system performance.
• Build AI solutions with observability, auditability, traceability, and human oversight in mind.
• Develop integrations with APIs, databases, cloud services, and other enterprise technologies.
• Troubleshoot and improve workflow accuracy, reliability, latency, and overall performance.
• Apply strong software engineering practices including testing, version control, modular development, and documentation.