Senior Lead Software Engineer

JPMorganChase•Jersey City, NJ

About The Position

If you're a Senior Lead Software Engineer who takes ownership of outcomes in production — not just implementation — and thrives on turning ambiguous requirements into stable, well-modeled service designs, this role was built for you. You will have meaningful latitude to influence architecture, engineering standards, and reliability posture across services, with expectations and recognition aligned to senior-level impact. As a Senior Lead Software Engineer at JPMorganChase within the Corporate AI/ML Data Platforms – Machine Learning Center of Excellence, you will design, build, and optimize high-performance, low-latency distributed systems that serve as the backbone of our machine learning and data infrastructure. You will collaborate across engineering, data science, and platform teams to deliver resilient, cloud-native solutions that enable the firm to operate at the forefront of AI-driven innovation. Your work will directly shape the reliability, scalability, and performance of systems that process critical data across the enterprise, and your voice will carry weight in the architectural and engineering decisions that define how the platform evolves.

Requirements

Formal training or certification on software engineering concepts and 5+ years’ applied experience; very strong Java development skills using object-oriented principles, with strong experience using Spring Boot
Demonstrated experience designing and tuning for low-latency processing in production distributed systems
Hands-on experience leveraging standard AWS compute, messaging, streaming, DB and storage services like MSK (Kafka), SQS, S3, ECS, EKS, Lambda, KVS/KDS, RDS, Dynamo, Redshift, and S3 in large-scale, resilient service architectures
Practical experience implementing alerting, monitoring, and reporting solutions using Datadog, Dynatrace, and/or Splunk in production-grade environments
Strong engineering fundamentals including API design, testing discipline, and debugging in production contexts
Strong ability in one or more modern programming languages (e.g., Java, Python, Go, Rust) with heavy emphasis on Java, writing clean, maintainable, OO, and testable code
Develops and maintains infrastructure-as-code solutions using Terraform and/or CloudFormation to support scalable, repeatable, and auditable cloud deployments
Strong experience with containerization and orchestration technologies, including Docker and Kubernetes
Demonstrated ability to communicate engineering tradeoffs clearly to both technical and non-technical stakeholders
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) 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, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.

Nice To Haves

Deep familiarity with low-latency, highly transactional architectures and advanced usage of AWS managed services (KVS/KDS) — particularly for real-time processing, distributed event handling, and efficient data storage and retrieval
Expertise designing and automating observability and reporting workflows using Datadog, Dynatrace, and Splunk to deliver actionable monitoring and production intelligence across microservices platforms
Experience with modern delivery practices including continuous integration and delivery, infrastructure-as-code, and containerized deployments that support reliable service delivery at scale
Experience with Terraform and/or CloudFormation for building and maintaining cloud infrastructure in an enterprise environment

Responsibilities

Architects and implements low-latency, high-throughput Java Spring Boot based distributed services, using object-oriented principles, that meet the performance demands of production-grade services with strong well-defined APIs
Designs and builds resilient, cloud-native service architectures with strong high-availability (HA) requirements, from 3 to 5 nines, leveraging standard AWS compute, messaging, streaming, DB and storage services like MSK (Kafka), SQS, S3, ECS, EKS, Lambda, KVS/KDS, RDS, Dynamo, Redshift, and S3.
Develops and maintains infrastructure-as-code solutions using Terraform and/or CloudFormation to support scalable, repeatable, and auditable cloud deployments
Implements and continuously improves observability solutions — including alerting, monitoring, and reporting — using Datadog, Dynatrace, and Splunk to deliver actionable production intelligence across microservices platforms
Translates ambiguous or evolving requirements into stable, well-modeled service designs, clearly articulating engineering tradeoffs to both technical and non-technical stakeholders
Leads technical design reviews, establishes engineering best practices, and drive adoption of standards that improve platform operability, reliability, and maintainability
Owns production outcomes end-to-end — identifying and resolving performance bottlenecks, reliability gaps, and scalability constraints through automation and runbook-driven operations
Partners with machine learning engineers and data scientists to understand platform requirements and deliver robust, production-ready engineering solutions
Mentors and provides technical guidance to engineers across the team, fostering a culture of ownership, continuous learning, and engineering excellence
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.