As an AI Engineer Intern, you'll be part of a dynamic team focused on developing, refining, and deploying core AI features for our proprietary AI-driven products and solutions. You'll bridge the gap between AI research and application, translating innovative concepts into tangible, impactful features.
Key Responsibilities:
Collaborate with cross-functional teams to design, develop, and implement AI features, focusing on areas like natural language processing, data analysis, and predictive modeling.
Conduct research and rapid prototyping of the most suitable AI algorithms and techniques based on best practices, particularly those involving LLMs to address technical and business requirements.
Write clean, well-tested, and well-documented code, contributing to efficient and maintainable system components.
Contribute to the project's foundational knowledge by documenting the shared technical understanding and implementation details aligned with high-level business requirements and project goals.
Qualifications:
Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence or a related field.
You are a team player that enjoys collaborating and communicating with diverse groups.
You enjoy tackling large challenges and enjoy discovering problems as much as solving them.
You are able to problem solve and adapt to changing priorities in a fast-paced dynamic environment.
Able to commit to a 6 months full time internship duration.
This is a full work-from-office (WFO) internship.
Skills You’ll Need to Bring:
You have built or prototyped features with AI technologies (LLMs, Embeddings, Fine-Tuning).
You have experience in agentic frameworks (e.g., ADK, LangChain) for building LLM-powered applications.
You have strong technical skills in Python.
You are adept at optimizing, refactoring, and improving Python code, ensuring our systems are efficient, scalable, and maintainable
Nice to Haves:
Hands-on experience with cloud platforms (GCP).
Demonstrated interest in advanced AI topics such as Reinforcement Learning (RL).
Familiarity with processing multi-modal data (audio and video).
Familiarity with Agile development methodology and collaboration tools (e.g., JIRA) for task management and sprint planning.
