Software Engineer - AI Focused

Analytics

• Responsible for designing, developing, and maintaining LLM-powered applications including application integration to key banking systems.
• The engineer is expected to be knowledgeable of current software packages related to the development of LLM-powered applications and is up-to-date with up and coming advances in the field. The engineer is expected to be well-verse with software development life cycle including requirements gathering, model development and integration, testing, and deployment.

• Lead the solution architecture and development of simple to complex applications that integrate Large Language Models (LLM) with banking core systems via advanced patterns (e.g., Agentic Orchestration, Multi-Agent Systems, RAG, and MCP integration, Prompt Engineering, etc. )
• Develop front-end web-based user interfaces that will expose Agentic applications to the workforce.
• Ensure reliability, usability, and responsiveness of the deployed application in the productionenvironment.
• Engineer robust LLM Evaluation frameworks and maintain accuracy standards and implement controls that safeguards the application from LLM-based hallucinations.
• Define engineering best practices (CI/CD, Unit Testing for AI, Code Reviews) and cascade design patterns and techniques to data engineers to foster an environment of knowledge
• sharing and co-development
• Develop and optimize full-stack interfaces (Front-end and API layers) that expose Agentic applications to the workforce, ensuring high availability and low latency.
• Conduct deep-dive research into emerging Agentic technologies and prototype their application within the bank's legacy infrastructure

• Degree in Mathematics, Statistics, Computer Science, Management Information Systems, or related field
• Experience building LLM-powered applications (production or strong POC).
• 4-5 years of total Software Engineering experience, with at least 2 years experience in developing, implementing, and deploying LLM-based applications
• Strong programming skills in Python. Proficiency in SQL for data transformation and analytics.
• Deep understanding of Vector Database architecture (e.g., Milvus, Pinecone) and advanced RAG retrieval strategies.
• Experience setting up LLM Ops / ML Ops pipelines (CI/CD, Evaluation, Monitoring)
• Familiarity with cloud technologies and deployment patterns (AWS and/or Google is preferred).
• Certificates and training in the field is highly desirable (e.g. Google AI certificate, Kaggle, OpenAI etc.)
• #LI-NC1