<div>Codemonk helps you to navigate the technical nuances by building amazing software solutions.</div>

CODEMONK

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 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
A) Generative AI&nbsp;
<ul>
<li>Algorithm Design &amp; Development: Design, implement, and debug machine learning algorithms with a focus on transformer-based models and generative AI applications</li>
<li>LLM API Integration: Build applications leveraging LLM APIs from OpenAI, Anthropic Claude, Gemini, and other foundation model providers</li>
<li>RAG Pipeline Development: Construct and fine-tune Retrieval-Augmented Generation (RAG) systems using vector databases such as FAISS, Pinecone, etc.</li>
<li>AI Application Deployment: Learn to deploy AI applications and models into production environments with proper monitoring and optimization</li>
</ul>
B) Deep Learning &amp; Computer Vision
<ul>
<li>Model Training &amp; Tuning: Working knowledge of training and fine-tuning deep learning models, including adapting pretrained models to new tasks.</li>
<li>Experimentation: Run and track experiments to compare models, parameters, and datasets, and draw clear conclusions from the results.</li>
<li>Data Preparation &amp; Audit: Help prepare, clean, and audit datasets for relevance, quality, and alignment with the problem being solved.</li>
<li>Model Evaluation: Evaluate models using appropriate metrics and identify where and why they underperform.</li>
<li>Optimization &amp; Deployment: Basic exposure to making models efficient and helping move them into production.</li>
</ul>
Required Technical Skills
<ul>
<li>Programming Proficiency: Strong foundation in Python programming with knowledge of any generative AI frameworks, including LangChain, LlamaIndex, and Hugging Face libraries, is a plus.</li>
<li>LLM Integration Experience: Familiarity with LLM APIs (OpenAI GPT-4, Anthropic Claude, Cohere) and interest in building AI applications</li>
<li>Fundamental Architecture Knowledge: Understanding of Deep Learning Fundamentals, including transformer-based models, attention mechanisms, and their optimization for various AI workloads</li>
<li>Embedding &amp; Vector Systems: Basic understanding of embedding models (OpenAI, sentence-transformers) and vector databases</li>
<li>Analytical Excellence: Strong problem-solving, debugging, and analytical skills with proven ability to work with complex algorithms and datasets</li>
</ul>