8 days ago

AI Engineer Trainee

Lensa

Hybrid
Intern
$0
Hybrid

Job Overview

Job TitleAI Engineer Trainee
Job TypeIntern
CategoryCommerce
Experience5 Years
DegreeMaster
Offered Salary$0
LocationHybrid

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Job Description

About the AI Engineer Trainee Program at FocusKPI

Lensa is promoting this AI Engineer Trainee program on behalf of FocusKPI Inc. This 3-month unpaid trainee program offers a unique opportunity to work at the intersection of AI innovation and real-world product development. You will master advanced AI techniques and sharpen your software engineering skills through hands-on experience and mentorship, contributing to actual product initiatives.

Program Focus Areas

  • Developing custom GPT-based agents for sales intelligence, document processing, and customer insight generation.
  • Optimizing LLM behavior and outputs through prompt engineering and prompt chaining.
  • Gaining experience in API design, CI/CD deployment, and scalable infrastructure.
  • Leveraging AI coding tools like Cursor and GitHub Copilot to accelerate development.
  • Working with LLM orchestration and agent frameworks such as LangGraph, CrewAI, and AutoGen.

You will apply these technologies to real customer use cases, including AI-powered CRM tools, medical device risk automation, and influencer marketing platforms.

Ideal Qualifications

  • Currently pursuing or having completed a Bachelor’s or Master’s in Computer Science, AI, Data Science, or related fields.
  • Eagerness to quickly learn and experiment with new AI technologies.
  • Active use of AI coding assistants like Cursor to speed up coding processes.
  • Hands-on experience or strong interest in ChatGPT, LLM APIs, and prompt design.
  • Familiarity with Python, FastAPI, and modern backend development practices.

Key Learning Opportunities

  • Architecting multi-agent systems using frameworks like LangGraph or CrewAI.
  • Designing and testing advanced LLM prompts for various tasks (classification, summarization, reasoning, task chaining).
  • Developing and deploying FastAPI applications that interface with LLMs.
  • Building RAG pipelines integrating LLMs with vector stores for contextual search.
  • Creating modular, scalable services to solve real business problems with AI.

Career Path After the Program

Exceptional trainees may be invited to a paid internship or long-term opportunity with FocusKPI, offering a direct pathway into AI product development with immediate impact.

Application Instructions

Please submit your resume and a brief cover letter outlining your experience with relevant projects, coursework/work experience, and your interest in this role directly to danz@focuskpi.com. Do not call. Be aware that FocusKPI's recruiting team will only use @focuskpi.com email domain for communication; report any fraudulent emails.

Key skills/competency

  • AI development
  • Machine Learning
  • Python programming
  • Prompt engineering
  • Large Language Models (LLMs)
  • FastAPI
  • Backend development
  • CI/CD
  • API design
  • Agent frameworks (LangGraph, CrewAI)

Tags:

AI Engineer Trainee
AI development
Prompt engineering
LLM orchestration
API design
CI/CD
Multi-agent systems
Product development
Python
FastAPI
LangGraph
CrewAI
GitHub Copilot
ChatGPT
Vector stores
Machine Learning
Data Science
Backend development
Unpaid Trainee

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How to Get Hired at Lensa

  • Research FocusKPI's vision: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor to align your application.
  • Showcase relevant projects: Highlight personal or academic projects involving AI, LLMs, or Python development in your resume and cover letter.
  • Emphasize AI tool proficiency: Explicitly mention your experience with AI coding assistants like Cursor or GitHub Copilot in your application.
  • Craft a compelling cover letter: Detail your interest in FocusKPI's AI Engineer Trainee program and how your background fits their specific needs.
  • Directly email your application: Submit your resume and cover letter to danz@focuskpi.com as instructed, strictly avoiding phone calls.

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