[Internal] AI Engineer Trainee

Job Title: AI Engineer Trainee
Location: Remote in United States (EST Preferred)
Type: Full-Time / Unpaid Trainee Program
Company: FocusKPI

Please submit your resume and a brief cover letter outlining your experience with relevant projects, coursework/work experience, and why you’re interested in this role to [email protected].
 

We are excited to invite aspiring software engineers and AI enthusiasts to join our 3-month AI Trainee Program, designed to fast-track your skills in building and implementing AI solutions.

This program provides hands-on experience with real-world projects, enabling you to enhance both your software engineering and AI expertise. Under the mentorship of our experienced team, you’ll refine your skills in designing scalable systems, deploying machine learning (ML) models, and building APIs.
Exceptional performance during the program may lead to a paid internship opportunity, setting the stage for a promising career in AI and software development.

This program is ideal for individuals passionate about AI and software engineering, particularly in Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), and software deployment. You’ll gain practical experience in building and deploying machine learning models, integrating them into vector-based solutions, and creating robust software infrastructure for scalable applications. The program focuses on your growth, providing you with the tools and knowledge to succeed in the dynamic field of AI-driven software development.

We welcome candidates with the following background:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or related fields.

• Interest and hands-on experience with NLP, RAG, and machine learning frameworks.

• Proficiency in Python and API development using frameworks like FastAPI.

• Strong problem-solving skills and a detail-oriented approach to software development.

• Gain hands-on experience in designing and implementing NLP models for tasks such as text classification, sentiment analysis, and named entity recognition.

• Learn and apply Retrieval-Augmented Generation (RAG) frameworks integrated with vector-based solutions to enhance system performance.

• Build and maintain scalable APIs (e.g., using FastAPI) for real-world deployment of AI models.

• Develop expertise in containerization tools like Docker to streamline deployment and scalability.

• Gain practical experience in implementing CI/CD pipelines for automated testing, integration, and deployment to ensure reliable and efficient delivery of AI solutions.

• Acquire practical knowledge in data preprocessing, feature extraction, and building robust data pipelines for large-scale applications.

• Work on integrating ML models into end-to-end production systems, focusing on scalability and performance.

• Strengthen your software engineering skills, with exposure to tools like Python, FastAPI, and cloud platforms (AWS, Google Cloud, Azure).

• Enhance your collaboration and documentation skills by working in a dynamic, remote team environment.