The posting
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.
- Experiment with emerging tools and frameworks across AI agents, orchestration, retrieval, and model evaluation.
- Collaborate with consultants, engineers, and data scientists to turn prototypes into scalable solutions.
- Share your work through technical documentation, demonstrations, and presentations.
- You work with technologies such as Python, OpenAI and Anthropic models, open-source LLMs, MCP, LangChain, LangGraph, Langfuse, cloud platforms, and containerized environments. Prior experience with all of these is not expected.
What You Bring
- Currently enrolled in final year or recently completed a Bachelor’s, Master’s, or PhD program in Computer Science, Machine Learning, AI, Data Science, Computational Linguistics, or a related field.
- Strong Python programming skills with solid software development, debugging, testing, and version-control fundamentals.
- Foundational full-stack experience building frontend, backend, APIs, and integrated application components.
- Familiarity with React and backend frameworks such as Node.js or FastAPI.
- Ability to turn AI prototypes into functional, end-to-end applications for real users.
- Strong problem-solving skills with a systematic approach to evaluating technical solutions and outputs.
- Ability to learn quickly, navigate ambiguity, and independently work through technical challenges.
- Clear communication skills with the ability to explain complex technical concepts simply & effectively.
- High ownership, curiosity, professionalism, attention to detail, and willingness to continuously learn.
- Hands-on exposure to generative AI, LLMs, AI agents, machine learning, or workflow automation
Nice to Have
Experience with any of the following is an asset, but not required:
- LLM application development or prompt engineering
- AI agents or orchestration frameworks
- Model Context Protocol (MCP)
- RAG architectures or vector databases
- AI evaluation and benchmarking
- Cloud platforms such as AWS, Azure, or GCP
- Docker or containerized applications
- Open-source LLMs
- Building software products, side projects, research projects, or technical prototypes
Compensation - $23/hour
Who can apply?
"This internship is supported by the Government of Canada's Student Work Placement Program (SWPP)”. To be eligible for SWPP funding, applicants must meet all three conditions given below:
- be a Canadian citizen, permanent resident, or person granted refugee protection in Canada;
- be enrolled as a full-time student at a recognized Canadian post-secondary institution for the duration of the placement [Dec 2026 to Apr 2027]; and
- be legally entitled to work in Canada. International students are not eligible for SWPP-funded placements.
Why ProCogia:
At ProCogia, you will work at the intersection of cloud, data, AI, and enterprise technology. You will solve real problems across commercial and regulated environments, work directly with clients, and take meaningful ownership from infrastructure design through production. You will also help shape the platform foundation supporting ProCogia’s growing AI capabilities, giving you exposure to technologies and challenges that extend well beyond traditional DevOps. This is an environment where strong engineers can build, learn, take ownership, and make a visible impact.
ProCogia is proud to be an equal-opportunity employer. We are committed to creating a diverse and inclusive workspace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
