Job Description

About ProCogia

ProCogia is a data and AI consulting firm helping organizations turn complex technology challenges into measurable business outcomes. We work with clients across highly regulated and high-stakes industries, including telecommunications, financial services, life sciences, healthcare, and the public sector, where security, governance, reliability, and performance matter.

Our teams combine data science, engineering, AI, analytics, and consulting expertise to build practical, production-ready solutions. We are actively advancing capabilities across generative AI, agentic systems, workflow automation, AI governance, and enterprise AI applications.

At ProCogia, interns work alongside experienced practitioners on meaningful technical problems. You will have the opportunity to experiment, build, challenge assumptions, and contribute directly to internal products and client solutions.

Location: Vancouver (On-site)

We are seeking two AI Interns (Co-op Students) to join our team from September – December 2026. 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 Computer Science, Software Engineering, Data Science, or related technical 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
    
    

Most importantly, you will gain practical experience building AI where accuracy, reliability, governance, and measurable performance matter just as much as innovation

Compensation - $23/hour