Apprentice

The interview process typically starts with a timed aptitude test, followed by a technical interview where candidates solve Python coding problems, discuss SQL queries, and demonstrate basic knowledge of LLMs and Power Platform. The final HR round assesses cultural fit, communication skills, and motivation. Preparation should focus on data manipulation, API basics, and clear articulation of project experiences.

GenAI & Automation promotes a collaborative, fast‑paced environment where learning is continuous and ideas are encouraged. Employees enjoy a balanced hybrid schedule, regular up‑skilling workshops, and clear growth pathways within American Express’s finance technology ecosystem.

Bachelor’s degree in Computer Science, Information Technology, Engineering or related field; minimum 60% aggregate (or CGPA 6.5/10); graduating batch 2023‑2026; no active backlogs; strong academic or project exposure in Python, SQL or data analysis; passion for AI and automation.

Round 1: Online aptitude & logical reasoning test → Round 2: Technical interview covering Python, SQL, Power Platform and GenAI fundamentals → Round 3: HR interview focusing cultural fit and career aspirations

GenAI & Automation is a cutting‑edge practice within American Express, focused on leveraging generative AI and low‑code platforms to transform finance‑centric processes. As part of a 175‑year legacy of innovation, the team works at the intersection of data, automation, and artificial intelligence to deliver faster, more accurate accounting, reconciliation, analytics, and reporting solutions for global enterprises. The culture encourages experimentation, rapid prototyping, and a strong governance mindset, ensuring that every AI‑driven workflow complies with regulatory and audit standards. The Apprentice role is designed for fresh graduates who are eager to dive into real‑world AI projects. You will assist senior engineers in building GenAI‑powered tools, develop Python scripts for data pipelines, and integrate Microsoft Power Platform components to automate repetitive tasks. This position offers a unique blend of hands‑on coding, low‑code automation, and exposure to large‑scale enterprise data, making it an ideal launchpad for a career in AI‑enabled finance technology. Key Responsibilities: 1. Assist in designing and implementing GenAI‑driven solutions for accounting, reconciliation, and reporting challenges. 2. Write and maintain Python scripts (pandas, numpy) for data extraction, transformation, and workflow automation. 3. Use SQL to query, validate, and analyze structured data from ERP and finance systems. 4. Experiment with large language models for summarisation, classification, and data‑extraction tasks. 5. Build and configure low‑code automations on Microsoft Power Automate and Power Apps. 6. Break down complex business problems into modular, automatable components. 7. Support prompt engineering, testing, and iteration for LLM integrations. 8. Document solutions to meet governance, audit, and compliance requirements. 9. Troubleshoot and maintain production‑grade automations. 10. Collaborate with cross‑functional teams to gather requirements and deliver end‑to‑end solutions. Tech Stack: Python (pandas, numpy), SQL, Microsoft Power Platform (Power Automate, Power Apps), Generative AI APIs (OpenAI, Anthropic), REST APIs, Git. Growth Path: Starting as an Apprentice, high performers can progress to Automation Engineer, then to AI Solutions Architect or Product Manager within the finance technology domain. Continuous learning opportunities include internal AI labs, certification programs, and mentorship from senior data scientists. Why Join GenAI & Automation? You will work on mission‑critical finance projects for a global brand, gain exposure to state‑of‑the‑art AI tools, and develop a versatile skill set that blends coding with low‑code automation. The hybrid model offers office collaboration three days a week while supporting remote productivity, and the company provides comprehensive well‑being benefits, free meals, and a robust training ecosystem.