Role Summary
We are seeking an experienced Python Lead to drive the design and development of Python-based enterprise solutions supporting Core Banking modernization. The role involves building scalable data processing frameworks, migration automation, cloud-native services, and AI-enabled solutions to accelerate operational efficiency. The ideal candidate should have strong expertise in Python, data engineering, cloud technologies, and a foundational understanding of AI/ML and LLM technologies to support future modernization initiatives.
Key Responsibilities
Lead the design and development of Python-based applications, automation frameworks, and enterprise utilities.
Develop scalable data ingestion, transformation, and migration solutions for high-volume banking data.
Build RESTful APIs and backend services using Python frameworks such as FastAPI or Flask.
Design and implement Kafka producers and consumers for real-time data streaming and event-driven architectures.
Develop automation solutions for data reconciliation, validation, monitoring, and operational support.
Integrate Python applications with Oracle, DB2, cloud platforms, and enterprise messaging systems.
Collaborate with Java, Mainframe, Data Engineering, and DevOps teams to support legacy modernization and cloud migration initiatives.
Optimize application performance, scalability, reliability, and observability across distributed environments.
Mentor development teams, conduct code reviews, and establish engineering best practices.
Support production deployments, incident resolution, and continuous improvement initiatives.
AI/ML & Intelligent Automation Responsibilities (Preferred)
Develop AI-powered automation solutions to streamline operational workflows, data validation, and engineering productivity.
Build and integrate Python-based services leveraging Generative AI, LLM APIs, or Retrieval-Augmented Generation (RAG) where appropriate.
Evaluate and prototype AI/ML use cases for code generation, document processing, knowledge retrieval, log analysis, and operational insights.
Integrate AI capabilities while ensuring compliance with enterprise security, governance, and data privacy standards, particularly for PII-sensitive environments.
Collaborate with enterprise architecture teams to identify future opportunities for Agentic AI adoption without impacting production stability
