Lead Python Application Developer - Vice President

Experience: 12+ years of hands-on application development experience, with a primary focus on Python, Data Structures and Algorithms.

Python Expertise: Expert-level Python 3.x proficiency, with deep knowledge of core concepts, advanced computation libraries, and design patterns (OOP, SOLID).

Frameworks: Advanced knowledge of Python web and API frameworks (FastAPI, Django, Flask).

Distributed Systems: Strong understanding of software architecture principles and distributed system design (Microservices, Event-Driven Architecture, caching strategies).

Database Systems: Extensive experience with relational databases (PostgreSQL, Oracle, SQL Server) and NoSQL databases (MongoDB, Cassandra, Redis), including advanced SQL query writing and optimization.

Operating Systems: Highly experienced with Unix/Linux-based operating systems, shell scripting, and system-level development.

Cloud Platforms: Strong background in cloud platforms AWS or IBM symphony grid.

Containerization & Orchestration: Hands-on experience with Docker containerization and contributing to Kubernetes orchestration.

CI/CD Pipelines: Proven exposure to and experience with Continuous Integration and Continuous Delivery (CI/CD) systems (Jenkins, GitLab CI, GitHub Actions, Bitbucket Pipelines) for automated deployment.

Testing & TDD: Proficiency in automated testing frameworks (Pytest, unittest) and Test-Driven Development (TDD) methodologies.

Security: Solid understanding of secure coding practices and application of security principles (OWASP Top 10).

Version Control: Expertise in Git (Bitbucket, GitHub) for advanced branching, merging, and pull request workflows.

Quantitative Computation: Multiple years of experience developing quantitative/calc-intensive Python solutions with advanced computation libraries (e.g., NumPy, Pandas, SciPy).

Risk Methodologies: Sound understanding of and exposure to various Risk Methodologies (e.g., Market Risk, Stress Testing, Value at Risk (VaR), Sensitivities).

Domain Delivery: Proven track record of delivering Stress Testing and Regulatory Risk projects in the Market Risk domain.

Mathematical Modeling: Exposure to mathematical modeling and financial insights.

AI/ML Frameworks: Exposure to AI/ML tools and frameworks.

Development Workflows: Practical experience utilizing AI tools (GitHub Copilot, Devin, Claude, Gemini, GPT) to optimize development workflows.

Agentic AI: Experience with Agentic AI tools, including writing Agents and Skills, and contributing to Agentic frameworks.

Generative AI: Understanding and practical application of Generative AI concepts in software development.

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