Work Model Onsite
Junior AI Engineer
Are you passionate about Artificial Intelligence and excited to build AI solutions that solve real-world business challenges? CGI is seeking a Junior AI Engineer to join our growing team in Salt Lake City, UT, where you’ll help design, develop, and deploy next-generation AI-powered applications for enterprise clients.
This is an exciting opportunity to work alongside experienced AI engineers, software developers, and architects while gaining hands-on experience with **Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and cloud-native technologies**.
If you’re curious, eager to learn, enjoy solving complex problems, and want to make an impact early in your career, we’d love to meet you.
At CGI, you’ll do more than write code—you’ll help organizations transform how they work with cutting-edge AI technologies while building a rewarding career in technology consulting.
Responsibilities
As a Junior AI Engineer, you’ll collaborate with experienced engineers, architects, and business teams to design, build, and deploy AI-powered solutions that address real business needs.
- Design and develop AI-powered product features using Generative AI, machine learning, and Large Language Models (LLMs)
- Build intelligent applications using Retrieval-Augmented Generation (RAG), embeddings, and vector database technologies
- Develop conversational AI experiences, AI agents, and agent-based workflows
- Build scalable REST APIs and FastAPI microservices to integrate and serve AI capabilities
- Develop and maintain AI workflows using LangChain, LangGraph, and other modern agentic frameworks
- Work with Model Context Protocol (MCP) servers and related tools to integrate AI applications with enterprise systems and data sources
- Containerize applications using Docker and support deployment in Kubernetes environments
- Contribute to cloud-native AI solutions designed for security, scalability, reliability, and maintainability
- Collaborate with product managers, software engineers, architects, and business stakeholders to deliver innovative AI capabilities
- Participate in code reviews, testing, troubleshooting, documentation, and continuous improvement initiatives
- Stay current with the rapidly evolving AI ecosystem and help evaluate emerging models, frameworks, tools, and development practices
Qualifications
Required:
- Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field, or equivalent practical experience
- Up to 2 years of professional, internship, academic, or project-based experience in software engineering, AI, or machine learning development
- Strong programming skills in Python and experience working with SQL
- Understanding of machine learning fundamentals and Generative AI concepts
- Hands-on experience working with Large Language Models (LLMs), including prompt engineering techniques
- Experience building or working with Retrieval-Augmented Generation (RAG) architectures and vector databases
- Familiarity with LangChain, LangGraph, or similar AI orchestration frameworks
- Familiarity with AI agents, agentic workflows, and common agent design patterns
- Experience working with or developing integrations using Model Context Protocol (MCP)
- Familiarity with AI configuration and instruction files, such as `AGENTS.md`, `SKILL.md`, or similar artifacts used to define AI system behavior and capabilities
- Understanding of REST APIs and microservices architecture
- Familiarity with Docker and modern CI/CD practices
- Strong analytical, problem-solving, collaboration, and communication skills
- Curiosity, adaptability, and enthusiasm for learning and applying emerging AI technologies
Skills:
- Agentic RAG
- Artificial Intelligence
- Large Language Model (LLM)
- Machine Learning
- Python
- SQL
Preferred:
- MLOps tools, workflows, and deployment practices
- Kubernetes and container orchestration
- FastAPI or similar Python web frameworks
- Cloud platforms such as Microsoft Azure, AWS, or Google Cloud
- Serverless and event-driven architectures
- High-availability, scalability, and autoscaling concepts
- AI evaluation frameworks, model benchmarking, and quality measurement
- AI security, governance, and responsible AI practices