AI/LLM Trainee Engineer

We are looking for a motivated AI/LLM Trainee Engineer to join a fully remote, two-month intensive training program designed to immerse aspiring engineers in the practical development of AI-driven solutions. This program combines structured learning with hands-on projects, preparing participants to transition into junior AI developer roles upon successful completion. Throughout the program, trainees will work closely with a Lead Developer mentor, gaining experience in Python programming, Azure cloud services, machine learning fundamentals, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), prompt engineering, and MLOps. The training culminates in a five-day hackathon-style capstone project where participants demonstrate their acquired skills by delivering an end-to-end AI application.

Key Responsibilities:
- Structured Learning: Complete a comprehensive curriculum focused on AI and machine learning foundations, emphasizing RAG, LLMs, prompt engineering, and MLOps principles.
- Hands-on Development: Build and prototype NLP and LLM-driven applications, progressively integrating them into larger systems.
- Proof-of-Concept Delivery: Develop internal proof-of-concept projects using Python and Azure services, following best practices in clean coding and version control.
- Capstone Execution: Design and implement a full AI solution within a five-day sprint, then present the project outcomes to the engineering team.
- Collaboration & Communication: Participate in regular check-ins with the Lead Developer, engage in code reviews, document work clearly, and actively seek feedback.
- Continuous Improvement: Stay updated on emerging tools and techniques relevant to project needs, demonstrating curiosity and proactive problem-solving.

Required Qualifications:
- Education: Bachelor’s degree or final-year student status in Computer Science, Software Engineering, Data Science, or a related field, or equivalent practical experience.
- Programming: Proficiency in Python, with the ability to write efficient, well-documented code and debug effectively.
- Machine Learning Fundamentals: Strong understanding of common machine learning algorithms, model evaluation methods, and concepts such as overfitting.
- Cloud Knowledge: Familiarity with Microsoft Azure fundamentals; experience with Azure Machine Learning or Azure Cognitive Services is a plus.
- AI/LLM Exposure: Demonstrated interest in natural language processing and large language models, including hands-on experimentation with GPT-style models via public APIs.
- Problem-Solving & Independence: Excellent analytical skills, self-motivation, and the ability to manage time effectively in a remote work environment.
- Communication: Clear written and verbal communication skills, with the ability to document and explain technical concepts to peers.

Preferred Qualifications and Benefits:
- Experience with AI frameworks such as Hugging Face Transformers or LangChain.
- Familiarity with web and API development in Python using frameworks like Flask or FastAPI.
- Exposure to containerization tools like Docker, CI/CD pipelines, and other MLOps technologies.
- Knowledge of vector databases such as Chroma, Pinecone, or Azure AI Search for implementing Retrieval-Augmented Generation.
- Prior participation in hackathons or capstone projects demonstrating the ability to deliver rapid, effective solutions.

Training Focus Areas:
- Retrieval-Augmented Generation (RAG): Building retrieval pipelines using embeddings and vector search to enhance LLM outputs.
- Large Language Models (LLMs): Utilizing and fine-tuning transformer-based models for various NLP tasks.
- Prompt Engineering: Developing and refining prompts, including prompt chaining techniques, to optimize model performance.
- MLOps & Deployment: Learning containerization, continuous integration/deployment, Azure-based AI service deployment, and production monitoring.

This program offers a unique opportunity to gain hands-on experience with cutting-edge AI technologies in a supportive, remote environment, setting the foundation for a career as a junior AI developer.