Director of Software Engineering(Java, AWS, ML Ops)
Description For Director of Software Engineering(Java, AWS, ML Ops)
JPMorgan Chase is seeking a Director of Software Engineering to lead their Consumer and Community Banking MLIO Team. This role combines technical leadership with machine learning expertise, requiring someone who can drive innovation while managing teams effectively. The position involves architecting and implementing machine learning solutions at scale, working with cutting-edge technologies, and influencing the bank's technical strategy. The ideal candidate will have extensive experience in software development, cloud platforms, and machine learning operations, along with strong leadership capabilities. This role offers the opportunity to impact one of the world's largest financial institutions while working with advanced technologies and leading talented teams. The position is based in Hyderabad, India, and comes with the backing of a 200-year-old institution known for its stability and innovation in financial services. As part of the Consumer & Community Banking division, you'll be contributing to services that directly impact millions of Chase customers across various financial products.
Responsibilities For Director of Software Engineering(Java, AWS, ML Ops)
- Define and execute the bank's machine learning strategy
- Manage a team of managers and individual contributors
- Lead machine learning engineers and own technical roadmap
- Drive innovation in machine learning solutions
- Act as thought leader and advisor to executive leadership
- Architect next-generation machine learning models and systems
- Create team processes and maintain high-quality code
- Promote software and model quality, integrity, and security
Requirements For Director of Software Engineering(Java, AWS, ML Ops)
- 10+ years of applied experience
- 5+ years of experience leading technologists
- Experience in building and scaling software/ML platforms
- Expertise in Java, Python and JavaScript
- Understanding of distributed systems and cloud-native architectures
- Experience in cloud platforms (AWS, GCP, Azure)
- Knowledge of ML Ops tools and practices
- Experience with CI/CD tools
- Experience with big data and pipeline tools
- Strong communication skills
- Expertise in Computer Science or related field
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