Senior Python Application Developer - Assistant Vice President

Lead Implementation: Lead the hands-on implementation and architecture of robust Python frameworks to build horizontally scalable, highly available, highly resilient, and performant applications.

Platform Engineering: Design and develop efficient software platforms for high-throughput data computation, processing, and quantitative calculations.

Standards & Alignment: Develop solutions adhering to enterprise architectural standards, utilizing in-depth technical and business domain knowledge.

Database & API Design: Design, develop, and secure high-performance RESTful APIs, integration patterns, and database schemas (relational and NoSQL).

SDLC & Code Quality: Enforce industry-standard SDLC best practices and contribute to code quality through rigorous code reviews, automated testing, and continuous integration.

Project Ownership: Take full ownership of technical initiatives, including planning, forecasting, project tracking, governance, and managing technology budgets at the team level.

Change Management: Drive continuous improvement, change management, and modern software engineering practices within the development team.

Independent Execution: Work independently, multi-task, and manage priorities effectively under pressure to meet tight deadlines or unexpected changes in requirements.

Cross-Functional Partnership: Act as a key liaison and partner with Risk Managers, Risk Analytics, Senior Quants, Front Office (FO), DevOps, and Production Support teams to ensure seamless IT deliveries.

Global Collaboration: Work effectively in a globally matrixed environment, partnering with virtual teams and external partners across different time zones.

Executive Communication: Consistently demonstrate clear, concise, and impactful written and verbal communication, ranging from technical discussions within the team to presentations for senior business executives.

Experience: 8+ 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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