Role Overview: We are seeking a highly motivated Software Engineering Student to join our team. This role focuses on the intersection of traditional software development and cutting-edge Artificial Intelligence. You will be responsible for developing, testing, and deploying hybrid systems that leverage Python and Large Language Models (LLMs) to solve complex problems. This is an ideal position for a student who is passionate about AI orchestration and building scalable, intelligent applications 

Key Responsibilities:

• AI Integration: Design and implement features that integrate LLMs (such as GPT, Llama, or Claude) into existing software architectures.
• Hybrid System Development: Develop and maintain "hybrid" applications that combine deterministic Python code with probabilistic AI outputs.
• Prompt Engineering & Fine-tuning: Optimize model performance through advanced prompting techniques and assist in the data preparation for model fine-tuning.
• Backend Development: Build robust APIs and microservices in Python to support AI-driven functionalities.
• Prototyping: Rapidly develop Proof of Concepts (PoCs) to test new AI capabilities and hybrid system workflows.

Required Skills & Qualifications:

• Active student from any of the last 2 semesters of Computer Systems, Software Engineering or similar.
• 90% English skills (Oral, reading, writing, and listening)
• Strong analytical and communication skills
• Advanced Python: Deep understanding of Python (asynchronous programming, decorators, type hinting, and packaging).
• AI/ML Literacy: Strong grasp of LLM concepts, including RAG (Retrieval-Augmented Generation), embeddings, and vector databases.
• Software Foundations: Solid understanding of Git, RESTful APIs, and software design patterns.
• Analytical Thinking: Ability to debug complex systems where the "output" isn't always predictable.
• TypeScript / JavaScript (React/Next.js): Most modern AI "chat" interfaces or dashboards are built using TypeScript, to ensure they can build full-stack prototypes, not just backend scripts.
• C++ (The "Performance" Layer): Most Python AI libraries (like PyTorch or TensorFlow) are actually written in C++ under the hood. If the student needs to optimize how a model runs on specific hardware, C++ is the language they’ll need.
• SQL and NoSQL (Data Management): AI is only as good as the data fed into it. A student should be comfortable querying databases to retrieve the context needed for LLMs. They will likely need to write complex queries to pull data for RAG (Retrieval-Augmented Generation) systems.
• Bash / Shell Scripting: Working with AI often involves managing environments, moving large datasets, and automating GPU-heavy tasks on Linux servers.

 

Preferred Qualifications:

• Experience with AI frameworks such as LangChain, LlamaIndex, or Haystack.
• Familiarity with containerization (Docker/Kubernetes) for deploying AI models.
• Knowledge of vector databases like Pinecone, Milvus, or Weaviate.