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Manager of Software Engineering - Java Full Stack, AI/ML

JPMorgan Chase
Bengaluru, IND
Full-time
Manager · 5+ years experience
Onsite
Discovered 1 weeks ago
java

Full Job Posting

Job responsibilities

  • Provides guidance to immediate team of software engineers on daily tasks and activities
  • Sets the overall guidance and expectations for team output, practices, and collaboration
  • Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams.
  • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives.
  • Anticipates dependencies with other teams to deliver products and applications in line with business requirements
  • Manages stakeholder relationships and the team’s work in accordance with compliance standards, service level agreements, and business requirements

Required qualifications, capabilities, and skills

  • Formal training or certification on software engineering concepts and 5+ years applied experience. In addition, demonstrated coaching and mentoring experience.
  • Experience leading technology projects. Experience managing technologists
  • Proficient in automation and continuous delivery methods
  • Proficient in all aspects of the Software Development Life Cycle. Experience with Full Stack Java project and good understanding of Data Engineering, AI/ML
  • Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
  • In-depth knowledge of the financial services industry and their IT systems
  • Practical cloud native experience.
  • Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data.
  • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage.

Preferred qualifications, capabilities, and skills

  • Experience working at code level, people management

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