Junior AI Engineer

CGI•Salt Lake City, UT

19h•Onsite

About The Position

Launch Your AI Career with CGI 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. Location: Onsite at the client location in the Salt Lake City, UT area

Requirements

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

Nice To Haves

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

Responsibilities

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