Job Summary
- 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.
How will you contribute
- 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
What will make you successful
- 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)
- Certificates and training in the field is highly desirable (e.g. Google AI certificate, Kaggle, OpenAI etc.)
