Python AI Intern

Role overview

Python AI Intern at Techolution

Responsibilities

• You'll work on production AI systems where Python proficiency directly impacts quality.

Employment details

• Location: Hyderabad [India]

Benefits

• 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

Additional role details

• 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.
• 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.