GenAI Engineer in Banking/Financial industryv

Capgemini is an AI-powered global business and technology transformation partner with nearly 60 years of heritage and over 420,000 team members in more than 50 countries. The company delivers end-to-end services and solutions leveraging deep industry expertise and a strong partner ecosystem, focusing on strategy, technology, design, engineering, and business operations. The Group reported 2025 global revenues of €22.5 billion.

• Practical banking/financial industry experience with Generative AI and/or Agentic AI, including LLM based workflows, prompt engineering, and orchestration of multi-step AI or agent workflows.
• Very good Python and SQL skills, with hands on experience in cloud based data platforms and databases, supporting end to end LLM based solutions.
• Good understanding of cloud platforms such as AWS, Azure or GCP.
• Strong communication skills and the ability to explain technical and AI related concepts to non-technical stakeholders.
• Degree in Finance, Mathematics, Data Science or a related quantitative or technical field.

Nice to Have:

• Hands on exposure to infrastructure and automation tools such as Terraform, GitHub CI/CD, Kubernetes or similar platforms.
• Familiarity with financial workflows such as risk modeling, valuation, forecasting, regulatory reporting or fraud detection.
• Basic knowledge of architecture design principles across IT and AI systems.

• Design and implement AI solutions, including Generative AI, LLM based workflows, and Agentic AI components, supporting automated and assisted decision making in financial processes.
• Execute end to end testing of AI solutions and AI agent workflows, ensuring accuracy, reliability, and alignment with business and regulatory expectations.
• Collaborate with SMEs and technical teams to design, implement, and optimize data pipelines for AI model training, inference, and agent execution.
• Evaluate model and agent performance, identifying opportunities for continuous improvement with a focus on accuracy, compliance, explainability, and iterative enhancement.
• Identify reusable Gen AI and Agentic AI components and patterns that can be leveraged across ongoing and future projects.