About Codemonk
Codemonk is a fast-growing product engineering and IT services firm that partners with clients to build scalable digital solutions. From startups to enterprises, we help businesses solve real-world challenges through cutting-edge technology and deep domain expertise. We've partnered with brands like Unilever, Kawada, Tata Power, greytHR, DMG, Experien, and more.
About Role
We are looking for a motivated Machine Learning Intern to join our team and assist in building and optimizing generative AI models. This internship offers hands-on experience in developing AI applications with the latest machine learning and natural language processing technologies.
Key Responsibilities
Algorithm Design & Development: Design, implement, and debug machine learning algorithms with focus on transformer-based models and generative AI applications
Model Benchmarking & Validation: Benchmark and validate performance of different AI models across various tasks and hardware configurations
LLM API Integration: Build applications leveraging LLM APIs from OpenAI, Anthropic Claude, Cohere, and other foundation model providers
RAG Pipeline Development: Construct and fine-tune Retrieval-Augmented Generation (RAG) systems using vector databases such as FAISS, Pinecone etc.
AI Application Deployment: Learn to deploy AI applications and models into production environments with proper monitoring and optimization
Required Technical Skills
Programming Proficiency: Strong foundation in Python programming with knowledge of generative AI frameworks including LangChain, LlamaIndex, and Hugging Face libraries
LLM Integration Experience: Familiarity with LLM APIs (OpenAI GPT-4, Anthropic Claude, Cohere) and interest in building AI applications
Transformer Architecture Knowledge: Understanding of transformer-based models, attention mechanisms, and their optimization for various AI workloads
Embedding & Vector Systems: Basic understanding of embedding models (OpenAI, Cohere, sentence-transformers) and vector databases (FAISS, Pinecone, Qdrant, Milvus)
Performance Optimization: Interest in model benchmarking, validation, and optimization across different hardware configurations
Analytical Excellence: Strong problem-solving, debugging, and analytical skills with proven ability to work with complex algorithms and datasets
