16221 - Junior AI Engineer
On-siteMidvale, Utah, United States
Job Summary
Design and develop AI-powered product features using Generative AI, Large Language Models, and Retrieval-Augmented Generation architectures. Build intelligent applications with vector databases, conversational AI experiences, and agent-based workflows while developing scalable REST APIs and FastAPI microservices. Develop and maintain AI workflows using LangChain, LangGraph, and Model Context Protocol servers to integrate applications with enterprise systems. Containerize applications using Docker for deployment in Kubernetes environments and collaborate with product managers and architects to deliver innovative AI capabilities. Participate in code reviews, testing, troubleshooting, and documentation while staying current with emerging AI models and frameworks.
Required Qualifications
- 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
Desired Qualifications
- 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
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