Bring your Software Engineering skills to the next level and join the AIML and Data Platforms team. As a Director of Software Engineering you will assist the AMDP team in overcoming the toughest challenges in financial services, with a view to delivering impact in the bank. You will help evolve projects from early-stage code into production. 

As Director of Software Engineering at JPMorgan Chase within the AMDP (AI/ML and Data platform) team,  you'll be a key member of an agile team dedicated to developing secure, stable, and scalable technology products. You'll lead critical technology initiatives across various business functions, supporting the firm's objectives. This hands-on technical leadership position involves collaborating with AI researchers in fields like Synthetic Data, Explainability (XAI), Fairness, Optimization, and Cryptography. Your primary focus will be on implementing the technology needed to scale AIML innovations within our top-tier investment and retail banking operations.

Job responsibilities 

Hands-on leadership and direction of a small team, charged with delivering innovation at scale Focuses on delivering the Synthetic Data product including Privacy Enhancement Techniques (PETs) and Cryptographic methods across the firm Integrates these seamlessly into the firm’s business systems, withing the constraints of a highly regulated and opinionated technology environment Creates robust pipelines for repeatable model delivery

Required qualifications, capabilities, and skills 

Formal training or certification on Software Engineering concepts and 10+ years applied experience Experience shipping 0-1 products Experience shipping products as python packages, running services Experience in working with large scale data and manipulating 100Ms of data at scale, reliably Exceptional Cloud Engineering skills (both public and private), and a proven track record of navigating a complex technical environment and delivering robust solutions within those constraints Up-to-date understanding of Model Development Lifecycle, and best practices for managing the fast-moving model provisioning environment Strong track record of developing high quality, production code in Python Advanced understanding of engineering methodologies such as CI/CD, Application Resiliency, Networking and Security Great interpersonal skills and able to interface with data scientists, quantitative researchers and other engineers Exceptional problem-solving skills in a complex technical environment Strong understanding of modern development practices 

Preferred qualifications, capabilities, and skills

Experience in Privacy Enhancement Techniques (PETs), Cryptography and implementing them as product deliverables Track record of Big Data specific infrastructure (e.g. Spark)  Experience integrating new tools/libraries into frameworks