AI/LLM Sr Manager of Software Engineering - Java and Python

JPMorgan Chase & Co.•New York, NY

About The Position

As a Senior Manager of Software Engineering at JPMorganChase within the Consumer and Community Banking Core Platform Engineering team, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.

Requirements

Formal training or certification on Software Engineering concepts and 5+ years applied experience.
In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise.
Experience leading teams of technologists.
Ability to guide and coach teams on approach to achieve goals aligned against a set of strategic initiatives
Strong software engineering foundation with hands-on experience building services in Java and Python, working with databases, and using modern integration patterns.
Working knowledge of AI/LLM-enabled application development, including prompt/context design, orchestration and agentic patterns, and evaluation/quality approaches.
Hands-on experience using AI coding assistants (e.g., GitHub Copilot, Claude Code, or firm-approved equivalents) to accelerate secure, high-quality development, test automation, and code review within enterprise guardrails.
Ability to set clear technical direction while remaining flexible and adaptive in a rapidly evolving problem space (e.g., AI-enabled product development).
Demonstrated experience partnering with product leaders and stakeholders to translate ambiguous requirements into actionable technical plans and measurable delivered results.
Experience hiring, developing, and recognizing talent, with a track record of building strong engineering culture and effective feedback mechanisms.
Practical cloud-native experience designing, deploying, and operating production systems on AWS.
Experience in Computer Science, Engineering, Mathematics, or a related field and expertise in technology disciplines.

Nice To Haves

Experience working at code level, including contributing to production-grade services, debugging complex issues, and setting engineering standards through exemplars.
Experience building AI-assisted and AI-native software systems, including LLM integration patterns, tool/skill design, retrieval/context enrichment, and agentic workflow orchestration.
Experience operating distributed systems in AWS (e.g., compute, storage, messaging, observability), with a track record of reliability and cost-aware scaling
Experience balancing delivery across multiple concurrent initiatives while managing dependencies across teams and stakeholder groups.
Experience with cloud-native DevOps practices, including CI/CD pipelines, infrastructure as code (e.g., Terraform, CloudFormation), and automated testing in distributed environments.

Responsibilities

Provide overall direction, oversight, and coaching for a team of mid-level software engineers delivering AI-enabled capabilities across the ProductGPT ecosystem, spanning basic to moderately complex tasks, and evolving toward more advanced agentic and workflow-based solutions
Lead with a hands-on mindset: stay close to design and implementation, unblock delivery, and drive engineering excellence through code reviews, architecture guidance, and pragmatic decision-making
Be accountable for decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures, especially as priorities shift in a fast-moving AI domain
Establish and iterate on AI development strategies for use-case delivery, including LLM integration approaches, evaluation strategies, prompt/context management, orchestration patterns, and safe deployment practices
Ensure successful collaboration across teams and stakeholders in multiple locations, partnering closely with product team representation to align on outcomes, milestones, tradeoffs, and sequencing
Manage and prioritize multiple concurrent requests and initiatives, balancing near-term delivery with platform health, operational readiness, and long-term technical direction
Drive cloud-native engineering practices leveraging AWS services and modern data/storage patterns, ensuring solutions are scalable, observable, secure, and cost-aware
Promote strong engineering fundamentals across the stack, including API design, data modeling, resiliency, testing discipline, CI/CD, and operational excellence
Identifies and mitigates issues to execute a book of work while escalating issues as necessary
Provides input to leadership regarding budget, approach, and technical considerations to improve operational efficiencies and functionality for the team
Creates a culture of diversity, opportunity, inclusion, and respect for team members and prioritizes diverse representation