SciTech Patent Art was founded to bridge a critical gap between the technical needs of R&D experts and the analytical demands of IP professionals. We recognized that solving complex patent issueswhether for prosecution, litigation, or innovationrequires meticulous analysis of thousands of patents and non-patent documents. Over two decades ago, we set out to tackle this challenge, blending deep technical expertise with innovative methodologies to deliver exceptional patent and non-patent search and analysis. Our mission has always been to empower clients with reliable, actionable insights that are efficient, accurate, and cost-effective.
We are seeking a motivated and enthusiastic AI/ML & Agentic AI Intern to join our team. You will work on real-world Artificial Intelligence, Machine Learning, and Agentic AI projects, assisting with data analysis, model development, AI agent creation, testing, and deployment. This role offers an excellent opportunity to gain hands-on experience with cutting-edge AI technologies, Large Language Models (LLMs), Coding Agents, and industry best practices.
- Assist in developing, training, evaluating, and optimizing Machine Learning models.
- Work with structured and unstructured datasets for analysis and model building.
- Perform data cleaning, preprocessing, and feature engineering.
- Support the implementation of AI solutions for real-world business challenges.
- Research and experiment with Machine Learning algorithms, Generative AI, and Agentic AI techniques.
- Design, develop, and test AI Agents capable of reasoning, planning, and executing multi-step tasks.
- Build AI workflows using Agentic AI frameworks and orchestration tools.
- Develop AI-powered assistants using Large Language Models (LLMs).
- Experiment with Retrieval-Augmented Generation (RAG), prompt engineering, function calling, tool usage, and multi-agent systems.
- Evaluate and improve AI agent performance, reliability, and response quality.
- Work with AI Coding Assistants and Coding Agents to accelerate software development.
- Build automation workflows using AI agents for software engineering and business processes.
- Develop Python-based AI applications integrated with LLMs and AI APIs.
- Collaborate with software teams to integrate AI agents into existing applications and workflows.
- Analyze large datasets to identify trends and actionable insights.
- Create reports, dashboards, and visualizations.
- Validate AI/ML model performance and recommend improvements.
- Develop and maintain Python-based AI/ML applications.
- Assist in integrating Machine Learning models and AI agents into software systems.
- Participate in testing, debugging, deployment, and documentation activities.
- Collaborate using Git-based development workflows.
- Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.
- Basic understanding of Machine Learning concepts and algorithms.
- Proficient programming skills in Python.
- Familiarity with libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch.
- Basic understanding of Large Language Models (LLMs) and Generative AI concepts.
- Knowledge of data structures, algorithms, statistics, and probability.
- Strong analytical and problem-solving skills.
- Effective communication and teamwork abilities.
- Passion for learning emerging AI technologies.
- Hands-on experience with Agentic AI frameworks such as LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Semantic Kernel, or similar.
- Experience using AI Coding Agents such as Claude Code, Cursor, GitHub Copilot, Windsurf, Cline, or similar developer tools.
- Experience with Prompt Engineering, RAG, AI workflows, and tool/function calling.
- Knowledge of Deep Learning, NLP, Computer Vision, or Generative AI.
- Familiarity with vector databases (FAISS, ChromaDB, Pinecone, Weaviate, etc.).
- Knowledge of SQL and database management.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with Git/GitHub.
- Understanding of REST APIs, Docker, model deployment, and MLOps concepts.
- Personal, academic, or open-source AI projects demonstrating practical AI/ML or Agentic AI development.
