Python AI Intern

We're building production-grade AI systems that require not just machine learning knowledge, but solid software engineering foundations. We need interns who can write clean, efficient, maintainable Python code, and leverage it to build intelligent systems with LLMs, RAG pipelines, and agentic workflows. You'll work on production AI systems where Python proficiency directly impacts quality. This isn't about learning Python on the job it's about applying strong Python fundamentals to build AI applications at scale. Build and optimize RAG systems for enterprise data processing and retrieval Design agentic workflows using LangChain & LangGraph for automated decision-making Develop Flask/FastAPI backends that integrate LLM capabilities into business processes Write clean, efficient, production-grade Python code that scales across high-volume transactions Optimize costs (LLM API calls, embeddings, compute) while maintaining accuracy Debug complex multi-step AI pipelines in production environments Implement caching strategies, error handling, and graceful degradation Monitor system performance and reduce latency bottlenecks Object-Oriented Programming: abstract classes, decorators, inheritance, SOLID principles Async/Await: understand concurrent vs. sequential execution, asyncio patterns Data Structures: arrays, linked lists, stacks, queues, heaps, graphs (can implement from scratch) Algorithm Optimization: write efficient solutions, analyze Big O complexity, recognize when O(n²) should be O(n log n) Error Handling: try/except best practices, logging (not just print statements) Can solve DSA problems optimally (not just getting them to work) Understand complexity trade-offs and implement accordingly Familiar with: binary search, DFS/BFS, quicksort/mergesort, dynamic programming, memoization LangChain: LCEL (chaining components), prompts, models, parsers, output validation LangGraph: state management (immutable patterns), nodes, conditional routing, agentic loops RAG Fundamentals: document chunking, embeddings, vector similarity, retrieval pipelines Production Mindset: caching, monitoring, error handling, cost optimization Proper testing, logging, git workflows. Vector databases (Pinecone, FAISS, Chroma, Weaviate) Fine-tuning experience (LoRA, PEFT, quantization) MLOps tools (MLflow, Weights & Biases) Graph databases (Neo4j) and knowledge graphs Cloud platforms (GCP) Deployed AI projects in production Specific domain knowledge (finance, healthcare, etc.) Multiple programming languages