Job description

When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this. As a Senior Manager of Software Engineering – Research & Development for the NEO and BRIE Platforms at JPMorgan Chase within the Commercial and Investment Banking – Data Analytics Payment Team , you lead multiple engineers and set the technical direction for how emerging technologies are evaluated, proven, and adopted across our agent runtime and data platform estate. You manage the team’s output, practices, and collaboration, and you are responsible for anticipating the needs of the platforms and the stakeholders they serve in a secure, stable, and scalable way. Job responsibilities • Provides overall direction, oversight, coaching, and career development for a team of 4–5 software engineers across varying experience levels, cultivating a high-performing R&D function • Owns the research and development agenda for the NEO agent runtime and BRIE data platforms, translating platform strategy into a prioritized book of work spanning query federation, policy, storage, cataloging, and caching/memory • Leads structured technology evaluations and proofs of concept — defining success criteria, benchmarking candidates, and producing clear recommendations that hold up to architectural and security review • Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and consistent validation standards (secure coding, peer review, automated testing), while promoting reuse of effective patterns across the team • Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns. • Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets. • Reviews and debugs code and designs authored by the team, and remains hands-on enough to guide critical technical decisions • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability within existing systems and information architecture • Makes decisions that influence team resources, budget, and tactical operations, and is accountable for those outcomes • Ensures successful collaboration across engineering teams, product, and platform stakeholders, and communicates findings and trade-offs to senior leadership • Adds to team culture of diversity, opportunity, inclusion, and respect Required qualifications, capabilities, and skills • Formal training or certification on software engineering concepts and 5+ years applied experience, with 2+ years leading and coaching teams of technologists • Hands-on practical experience delivering system design, application development, testing, and operational stability • Experience hiring, developing, and recognizing talent, and setting expectations for team output and engineering practices • Advanced in one or more programming language(s): Java, Python, or Rust • Demonstrated experience leading effective use of approved AI-assisted software development tools, with the ability to set team expectations for validating AI outputs for correctness, performance, and security • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations and secure handling of inputs/outputs • Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs. • Proficient in all aspects of the Software Development Life Cycle • Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security • Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, distributed data systems) • In-depth knowledge of the financial services industry and their IT systems Preferred qualifications, capabilities, and skills • Experience evaluating and operating query federation engines (e.g., Starburst/Trino) across heterogeneous data sources • Experience with centralized policy and authorization engines (e.g., OPA/Rego, OpenFGA) for runtime and structural access control • Experience deploying and tuning columnar/OLAP stores (e.g., ClickHouse) in both on-premises and AWS environments • Familiarity with open-source data catalog solutions and open table formats (Apache Iceberg) for lakehouse architectures • Experience with caching and in-memory data solutions (e.g., Redis) for low-latency retrieval and agent/session memory • Exposure to LLMs, RAG architectures, vector databases, and embedding-based retrieval systems • Proficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes) Serve in a leadership role by providing technical coaching and advisory for multiple technical teams

Requirements

Must-haves

When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this.
Provides overall direction, oversight, coaching, and career development for a team of 4–5 software engineers across varying experience levels, cultivating a high-performing R&D function
Leads structured technology evaluations and proofs of concept — defining success criteria, benchmarking candidates, and producing clear recommendations that hold up to architectural and security review
Reviews and debugs code and designs authored by the team, and remains hands-on enough to guide critical technical decisions
Makes decisions that influence team resources, budget, and tactical operations, and is accountable for those outcomes
Ensures successful collaboration across engineering teams, product, and platform stakeholders, and communicates findings and trade-offs to senior leadership
Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience, with 2+ years leading and coaching teams of technologists
Hands-on practical experience delivering system design, application development, testing, and operational stability
Experience hiring, developing, and recognizing talent, and setting expectations for team output and engineering practices
Advanced in one or more programming language(s): Java, Python, or Rust
Demonstrated experience leading effective use of approved AI-assisted software development tools, with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations and secure handling of inputs/outputs
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, distributed data systems)
In-depth knowledge of the financial services industry and their IT systems
Experience evaluating and operating query federation engines (e.g., Starburst/Trino) across heterogeneous data sources
Experience with centralized policy and authorization engines (e.g., OPA/Rego, OpenFGA) for runtime and structural access control
Experience deploying and tuning columnar/OLAP stores (e.g., ClickHouse) in both on-premises and AWS environments
Familiarity with open-source data catalog solutions and open table formats (Apache Iceberg) for lakehouse architectures
Experience with caching and in-memory data solutions (e.g., Redis) for low-latency retrieval and agent/session memory

Nice-to-haves

Preferred qualifications, capabilities, and skills

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aijavapythonrustagileci/cdmachine learningawsdockerkubernetes

Platform: oracle-cloud
Effort: medium