AI Engineer Trainee (Remote)
FocusKPI Inc.
- Boston, MA
- Training
- Full-time
Type: Full-Time / Unpaid Trainee Program
Company: FocusKPIDO NOT CALL US. We only consider your application if you 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 .Build the Future of AI with Agents & Applied Engineering.Are you eager to work at the intersection of AI innovation and real-world product development? Our 3-month AI Trainee Program is your gateway to mastering advanced AI techniques—like multi-agent systems, ChatGPT-style prompting, Retrieval-Augmented Generation (RAG)—while sharpening your software engineering skills through hands-on experience and mentorship.This is more than just a training program—it's a fast track into the next generation of AI development.About the Program:You’ll be working on actual product initiatives using the latest advancements in:
- LLM orchestration and agent frameworks (LangGraph, CrewAI, AutoGen)
- Custom GPT-based agents that support use cases like sales intelligence, document processing, and customer insight generation
- Prompt engineering and prompt chaining to optimize LLM behavior and outputs
- Vector databases and RAG pipelines for knowledge retrieval and augmentation
- API design, CI/CD deployment, and scalable infrastructure
- Are pursuing or have completed a Bachelor’s or Master’s in Computer Science, AI, Data Science, or related fields
- Have hands-on experience or strong interest in ChatGPT, LLM APIs, and prompt design
- Are familiar with Python, FastAPI, and modern backend development
- Are comfortable with Git, basic cloud deployment (AWS/GCP), and containers (e.g., Docker)
- Bonus: Experience with Langchain, LangGraph, or vector DBs like Pinecone, Weaviate, or FAISS
- Architect multi-agent systems using frameworks like LangGraph or CrewAI
- Design and test advanced LLM prompts for classification, summarization, reasoning, and task chaining
- Develop and deploy FastAPI applications that interface with LLMs
- Build RAG pipelines that integrate LLMs with vector stores for contextual search
- Automate workflows using tools like n8n or MCP protocol to integrate data across platform
- Create modular, scalable services that solve real business problems using AI
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