Senior Lead Software Engineer

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.

Job 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 e...