•Architect, design, and deliver scalable, Python-based applications supporting credit risk analytics, workflows, and reporting.
•Partner with risk, product, and technology leadership to integrate platforms, identify enhancements, and enable new products and process improvements.
•Resolve high-impact, complex initiatives through deep analysis of business processes, system flows, and industry standards.
•Ensure solutions adhere to enterprise architecture, data, security, and infrastructure blueprints.
•Establish and enforce engineering standards for coding, testing, CI/CD, debugging, and production readiness.
•Design and evolve microservices-based architectures, ensuring scalability, resiliency, observability, and maintainability.
•Apply AI and GenAI capabilities to modernize workflows, automate analysis, and unlock new insights in credit risk.
•Serve as technical leader and mentor, coaching mid-level engineers and analysts and allocating work as needed.
•Apply sound risk and control judgment, ensuring compliance with laws, regulations, and policies while safeguarding clients, data, and the firm.
How You’ll Work
•Operate with a startup mindset: ownership, bias for action, and pragmatic innovation.
•Deliver with enterprise discipline: stability, controls, transparency, and audit readiness.
•Balance experimentation with responsibility in a high-trust, high-impact environment.
•Adapt quickly as priorities change while maintaining long-term system integrity.
Core Technical Skills
•10–15 years of experience in application development or systems engineering within complex environments.
•Advanced proficiency in Python and SQL , with strong software engineering fundamentals.
•Hands-on experience building API-driven services using FastAPI, Pydantic, and/or Django.
•Proven expertise designing and implementing microservices architectures, including service decomposition, inter-service communication, resiliency patterns, and observability.
•Strong experience with Docker and Kubernetes , deploying and operating containerized services in production.
•Deep understanding of system architecture, data flows, and distributed systems.
•Experience working in Linux environments, including shell scripting and operational troubleshooting.
•Strong track record implementing unit testing, TDD, and automated quality controls.
•Subject Matter Expert (SME) in at least one application, platform, or service domain.
AI & Modern Engineering Tools
•Working knowledge of large language models (LLMs) and modern AI platforms from leading providers such as OpenAI, Anthropic, Google, and Meta.
•Experience designing or contributing to LLM-enabled solutions (e.g., copilots, workflow automation, analytics augmentation).
•Familiarity with prompt engineering, model integration patterns, and AI governance considerations in enterprise environments.
•Exposure to modern “vibe coding” practices—leveraging AI-assisted tooling to accelerate development, experimentation, and problem solving while maintaining engineering rigor.
