AI Engineer.

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.