AI Developer - Graduate Intern

Company Description Data Foundations is an intelligent data platform that unifies semantics, ontologies, knowledge graphs, active metadata, and agentic AI into a single environment. The platform enables organizations to connect diverse data sources and interact with their data using plain English queries. Users receive governed, explainable, and source-verified answers in seconds, supporting confident decision-making and compliance. As an innovation-focused company, Data Foundations offers opportunities to work on cutting-edge AI technologies that bridge data management and real-world business needs.Role Description What you'll doAI and data* Build and test features using large language models (LLMs), including prompt design, retrieval-augmented generation (RAG) and simple agent workflows* Integrate AI services on AWS, such as Amazon Bedrock, SageMaker, Textract and Comprehend* Prepare, clean and transform data with Python and SQL, and help build data pipelines* Help evaluate model outputs for accuracy, bias and cost, and log resultsFull-stack development* Build responsive front-end interfaces with React (or a similar framework) and TypeScript* Develop back-end APIs and services in Python (FastAPI/Flask) or Node.js* Work with relational and NoSQL databases (e.g. PostgreSQL, MySQL, DynamoDB)* Deploy and run services on AWS using Lambda, API Gateway, S3, ECS and infrastructure as code (CDK, CloudFormation or Terraform)Engineering practice* Write clean, tested, documented code and take part in code reviews* Use Git and CI/CD pipelines to ship changes safelyQualificationsMust be a Graduate/Masters student or a recent Graduate pursuing or recently completed a degree in Computer Science, Data Science, AI, or a related technical field.Knowledge of Pattern Recognition and Neural Networks, with the ability to design, train, and evaluate machine learning models.Familiarity with Natural Language Processing (NLP), including text processing, embeddings, and language model concepts.Understanding of AI/ML fundamentals, statistics, and model evaluation techniques.Experience with common ML and NLP libraries (e.g., PyTorch, TensorFlow, scikit-learn, spaCy, Hugging Face) is beneficial.Comfort working in a remote, collaborative environment with version control tools (e.g., Git) and agile workflows.Strong analytical thinking, clear technical communication, and eagerness to learn emerging AI and data technologies.