About the job
As a Market Risk Technology Director, you will lead the development of Python-based AI applications and frameworks that empower business teams to onboard their own datasets and seamlessly leverage advance tooling. Your solutions will provide a platform for intuitive data access, AI-driven insights and automation working closely with the Market Risk business teams to help gain productivity and make faster, smarter decisions.
Responsibilities
Build and own Python-based AI applications leveraging natural language interfaces, Large Language Models, data analytics, and automation.
Deliver innovative applications and tools that allow business users onboard and manage their datasets and leverage self-service analytics.
Architect and manage RESTful services for large-scale enterprise solutions using open standards.
Optimize data ingestion and processing pipelines for efficiency and performance.
Collaborate with Firm Risk Management stakeholders to understand requirements and deliver AI solutions.
Provide technical leadership and mentorship to developers within the agile squad.
Qualifications
Minimum
Strong hands-on application development experience in Python, including building service APIs using frameworks such as FastAPI or Flask.
Proven experience designing and delivering production-grade AI or LLM-based applications.
Solid understanding of data engineering fundamentals including SQL, data modeling and data lifecycle management.
Hands-on experience with concurrency and performance optimization (multiprocessing, multithreading, asynchronous I/O).
Practitioner of unit testing, integration and performance testing.
Experience owning systems in production, including reliability, incident management, and continuous improvement.
Ability to set technical direction make architectural decisions, and mentor senior engineers.
Bachelor’s or Master’s degree in Computer Science or related field, or equivalent experience.
5+ years of related experience.
Preferred
Experience with agentic workflows, natural language or “talk-to-data” systems.
Hands-on experience with OpenTelemetry and observability tooling (Grafana, Prometheus).
Experience with cloud-native architectures, including Docker and Kubernetes
Familiarity with data streaming, messaging, or caching technologies (Kafka, Redis).
Exposure to CI/CD, DevOps, or GitOps practices (e.g., Jenkins, GitHub Actions).