Manager of Software Engineering - Java Full Stack, AI/ML
Manager of Software Engineering - Java Full Stack, AI/ML
JP Morgan Chase & Co.- Posted 7 hours ago
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Job Description
This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions.
As a Manager of Software Engineering at JPMorgan Chase within theCommercial & Investment Bank, Payments Technology, you lead multiple teams and manage day-to-day implementation activities by identifying and escalating issues and ensuring your team's work adheres to compliance standards, business requirements, and tactical best practices.
Job responsibilities
As a Manager of Software Engineering at JPMorgan Chase within theCommercial & Investment Bank, Payments Technology, you lead multiple teams and manage day-to-day implementation activities by identifying and escalating issues and ensuring your team's work adheres to compliance standards, business requirements, and tactical best practices.
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
More Info
Job Type:
Function:
Employment Type:
Key Skills
Application Resiliency
TLM automation
CI/CD
Automation and continuous delivery methods
AI-assisted engineering practices
Cloud native experience
About Company
Skills:
data engineering , Ml, Agile Methodologies, Full Stack Java, Application Resiliency, Ai, CI CD, Security, TLM automation, Automation and continuous delivery methods, AI-assisted engineering practices, Cloud native experience
Skills:
Retrieval-Augmented Generation (RAG), Machine Learning, SAS, Sql, Deep Learning, MLops, Excel, Python, LLM behavior control, model evaluation monitoring, LLMOps, prompt engineering, Gen AI, Agentic AI, R, Responsible AI
Skills:
Algorithms, Software Development, Debugging, data structures, Programming Languages, ML infrastructure, model evaluation, Data Processing, Optimization, model deployment, reinforcement learning
Skills:
Azure ML, React, Docker, Distributed Systems, Python, Kubernetes, software engineering best practices, DL fundamentals, Go, Gradio, vector databases, classical ML, MLOps practices, GCP Vertex AI, AWS SageMaker, Streamlit
Skills:
Data Science, Machine Learning, Artificial Intelligence, Python, Deep Learning, AI ML Tools, Digital Learning Technologies, Instructional Design, LMS Platforms, Curriculum Development, Technical Content Creation