AI Engineering Intern - Tractian (Remote)
1 day ago⚠️ Before applying, please carefully review all the job requirements
- Location:
- Atlanta, Georgia, United States
- Work Arrangement:
- Remote
- Experience:
- Fresher
- Job Type:
- Internship
- Company:
- Tractian
- Salary:
- Negotiable
- Posted:
- 2026-10-02
Job Overview
- Tractian is seeking a Generative AI Engineering Intern to join its software development organization in a remote capacity. Tractian is an industrial technology company recognized on the Forbes AI 50 and Deloitte's Technology Fast 500, delivering predictive maintenance solutions through hardware, software, and artificial intelligence across more than 1,200 facilities globally.
- In this role, the intern will focus on developing and refining backend systems powered by large language models (LLMs). The position blends server-side software engineering, API integration, and retrieval-augmented generation (RAG) implementation. Working alongside software and product engineers, the intern will build robust data connectors, create automated tests, debug system failures, and optimize application performance.
Key Responsibilities
- ▪ Construct server-side software modules connecting large language models with external APIs and data repositories.
- ▪ Assist in building, enhancing, and refining retrieval-augmented generation (RAG) architectures.
- ▪ Author comprehensive test scripts, diagnose code failures, and increase overall application stability and throughput.
- ▪ Work closely with product and engineering groups to facilitate system integration, deployment routines, and continuous monitoring.
Required Skills and Qualifications
- ▪ Current enrollment in or recent completion of a Bachelor's, Master's, or PhD degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related technical discipline.
- ▪ Demonstrated proficiency in backend programming, software debugging, database management, and version control using Git.
- ▪ Hands-on experience developing a practical LLM-based project with the ability to detail technical choices, individual contributions, and verification methods.
- ▪ Solid comprehension of retrieval-augmented generation (RAG) principles and methods for linking language models with external information sources.
Helpful Experience
- ▪ Hands-on familiarity with TypeScript or Go programming languages.
- ▪ Practical exposure to vector database systems, PyTorch frameworks, Docker containerization, or cloud platform deployments.
- ▪ Basic understanding of large language model inference processes or optimization techniques.
What You Will Gain
- ▪ Direct experience building, testing, and deploying production-grade LLM applications utilizing RAG and API architectures.
- ▪ One-on-one engineering mentorship focusing on backend development, automated testing, and system reliability.
- ▪ Practical understanding of real-world AI application monitoring, deployment workflows, and system maintenance.
Application Tips
- Applicants may want to highlight their hands-on projects involving large language models, RAG pipelines, or backend API development on their CV. Detailing specific contributions, architectural choices, and testing methodologies used in personal, academic, or research projects can effectively demonstrate technical readiness. Candidates should emphasize proficiency in core backend concepts, database handling, and Git version control workflows. Additionally, mentioning any familiarity with TypeScript, Go, PyTorch, vector databases, or Docker can help showcase a well-rounded skill set relevant to Tractian's engineering stack.
Prepare for Your Interview
- These suggested questions can help you prepare for an interview by focusing on the skills, responsibilities, and technical areas relevant to this role.
Suggested Interview Questions
- Q: How do you structure a Retrieval-Augmented Generation (RAG) pipeline to connect an LLM with external data sources?
- Q: Can you walk through a working LLM project you built, explaining your technical decisions and testing methodology?
- Q: What strategies do you use for debugging and troubleshooting backend API integrations when unexpected failures occur?
- Q: How do vector databases function within a generative AI application, and how do they differ from traditional relational databases?
- Q: What approaches do you take to measure and improve the reliability and performance of an LLM-powered backend?
- Q: How do you handle version control and code collaboration using Git during backend software development?
- Q: What experience do you have using containerization tools like Docker or cloud services for deploying backend microservices?
- Q: How do you approach writing automated tests for non-deterministic AI outputs or language model responses?
Ideal Candidates Fill the Job Form: Apply Here
