This is an early IT career; hands-on role designed for candidates who want to build a strong foundation in Artificial Intelligence and Generative AI systems. You will contribute to real-world AI solutions—such as retrieval-based applications, internal copilots, and workflow automation—while learning enterprise-grade engineering practices in a regulated financial services environment. This is an early IT career; hands-on role designed for candidates who want to build a strong foundation in Artificial Intelligence and Generative AI systems. You will contribute to real-world AI solutions—such as retrieval-based applications, internal copilots, and workflow automation—while learning enterprise-grade engineering practices in a regulated financial services environment. What You’ll Do • Assist in building and enhancing Generative AI applications such as RAG-based solutions, internal copilots, and AI-powered workflows. • Contribute to prompt development, testing, and basic evaluation of LLM outputs. • Support implementation of data ingestion, chunking, embeddings, and retrieval pipelines. • Write and maintain clean, well-tested code using Python. • Work with cloud-native services (Azure and/or AWS). • Participate in CI/CD pipelines and automated testing. • Learn and follow Responsible AI, security, and compliance guidelines. • Collaborate with cross-functional teams to understand business use cases. • Document implementations and contribute to knowledge sharing. • Stay current with emerging GenAI tools and practices. What You Need to Have • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, AI/ML, or a related field. • 0–5 years of experience in software development, data engineering, or AI/ML (internships count). • Foundational understanding of AI and machine learning concepts. • Exposure to Generative AI concepts such as LLMs, embeddings, or retrieval-based systems. • Hands-on experience with Python. • Familiarity with cloud concepts (Azure or AWS). • Knowledge of Git and software development workflows. • Strong problem-solving skills and eagerness to learn. • Good communication and teamwork skills. Nice to Have • Projects involving chatbots, LLMs, or GenAI applications. • Exposure to Azure OpenAI, OpenAI APIs, Hugging Face, LangChain, or vector databases. • Basic knowledge of DevOps or MLOps concepts. • Familiarity with information retrieval or search. • Exposure to financial services or regulated industries. What Will Really Catch Our Eye • Personal or academic AI/GenAI projects. • Demonstrated ability to learn new tools quickly. • Clear documentation or presentations of technical work. • Evidence of collaboration or peer mentoring. • Passion for solving real-world business problems with technology.