Machine Learning Intern (AI/ML)

About Codemonk

Codemonk is a fast-growing product engineering and IT services firm that partners with clients to build scalable digital solutions.

About The 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

Generative AI Focused (primary focus)

• Design, implement, and debug machine learning algorithms with a focus on transformer-based models and generative AI applications.
• Benchmark and validate performance of different AI models across various tasks and hardware configurations.
• Build applications leveraging LLM APIs from OpenAI, Anthropic Claude, Cohere, and other foundation model providers.
• Construct and fine-tune Retrieval-Augmented Generation (RAG) systems using vector databases such as FAISS, Pinecone, etc.
• Learn to deploy AI applications and models into production environments with proper monitoring and optimization.

Deep Learning & Computer Vision (supporting focus)

• Working knowledge of training and fine-tuning deep learning models, including adapting pretrained models to new tasks.
• Run and track experiments to compare models, parameters, and datasets, and draw clear conclusions from the results.
• Help prepare, clean, and audit datasets for relevance, quality, and alignment with the problem being solved.
• Evaluate models using appropriate metrics and identify where and why they underperform.
• Basic exposure to making models efficient and helping move them into production.

Computer Vision Focused (primary focus)

• Design, train, and debug deep learning models for tasks like image classification, object detection, segmentation, and tracking, using architectures such as CNNs, Vision Transformers, and YOLO-family models.
• Build and maintain image and video datasets, including collection, annotation workflows, cleaning, and augmentation.
• Run experiments to compare architectures, hyperparameters, and training strategies, including transfer learning and fine-tuning of pretrained models.
• Evaluate models with metrics like mAP, IoU, precision/recall, and F1, and benchmark accuracy, latency, and throughput across hardware configurations.
• Optimize models for production and edge using quantization, pruning, and export formats like ONNX and TensorRT, and help deploy them with proper monitoring.

Generative AI (supporting focus)

• Working familiarity with using large language models through APIs or open-source libraries.
• Basic understanding of how retrieval and generation work together to build knowledge-grounded applications.
• Write and refine prompts, and connect model calls into simple application workflows.
• Assess the quality, accuracy, and reliability of generated outputs.
• Interest in models that combine vision and language, and how they apply to real-world use cases.

Requirements & Tech Stack

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.
• Fundamental Architecture Knowledge: Understanding of Deep Learning Fundamentals including 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.
• Strong Python programming skills and comfort working with large datasets.
• Solid grasp of deep learning fundamentals: neural networks, backpropagation, loss functions, optimizers, and regularization.
• Good understanding of computer vision concepts: convolutions, feature extraction, and common architectures (ResNet, EfficientNet, ViT, YOLO, U-Net).
• Hands-on experience with PyTorch (or TensorFlow) and libraries like OpenCV, torchvision, or Ultralytics.
• Familiarity with transformer architecture and attention mechanisms.
• Basic awareness of generative AI and multimodal models, and an interest in applying them to vision problems.
• Strong problem-solving, debugging, and analytical skills.

Note: This is a paid internship.