Senior Lead Software Engineer - Java, AWS & AI Platform Services

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 150,000 - 240,000; Full time

Job summary

JPMorganChase invites a Lead Software Engineer to join the Machine Learning Center of Excellence and help design trusted, scalable AI platforms at global scale. You will work in an agile environment within Corporate AI and ML Data Platforms to deliver high-impact engineering solutions.

The role emphasizes production-grade Python services, cloud-native architectures on AWS, and collaboration across product managers, platform teams, and SREs to achieve secure, reliable deployments.

Qualifications

• Formal training or certification in software engineering with 5+ years of applied experience.
• Advanced proficiency in Python, OO design, and modular architecture.
• Experience building large-scale cloud-native services in AWS.
• Hands-on with EKS, ECS, MSK (Kafka), SQS, and S3.
• Strong IaC experience with Terraform and/or CloudFormation.
• Experience with observability platforms (Datadog, Dynatrace, Splunk).
• Strong API design, microservices, scalable patterns.
• Experience with CI/CD pipelines and secure-by-design practices.
• Experience with AI-assisted software development tools.
• Understanding of responsible AI, security, resiliency, and compliance; mentoring engineers.

Responsibilities

• Design, develop, and maintain production-grade Python services and APIs powering ML platforms.
• Architect and implement high-throughput, low-latency distributed systems in AWS.
• Build scalable cloud-native apps using AWS technologies (EKS, ECS, MSK, SQS, S3).
• Develop reusable service frameworks and shared libraries across teams.
• Implement infrastructure-as-code with Terraform and CloudFormation.
• Create monitoring and observability using Datadog, Dynatrace, and Splunk.
• Deploy and support applications in production with SLAs.
• Apply secure-by-design practices and automated testing including blue/green and canary deployments.
• Review code and mentor engineers on best practices.
• Collaborate with product managers and platform SREs to deliver scalable solutions.
• Drive adoption of enterprise AI-assisted engineering practices.

Skills

Python

API design

Distributed systems

AWS

Terraform

CloudFormation

CI/CD

Observability

Secure coding

AI tools

Education

Formal software engineering training

Tools

EKS

ECS

MSK

SQS

S3

Datadog

Dynatrace

Splunk

Terraform

CloudFormation

Job description

Join JPMorganChase's Machine Learning Center of Excellence — where cutting-edge engineering meets real-world impact at global scale.

As a Lead Software Engineer at JPMorganChase within the Corporate Sector – Artificial Intelligence and Machine Learning Data Platforms and Machine Learning Center of Excellence team,you serve as a seasoned member of an agile team to design and deliver trusted, market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will collaborate with a multi-disciplinary community of experts focused exclusively on machine learning, working with cutting-edge techniques in disciplines such as deep learning and reinforcement learning.

Job responsibilities

• Design, develop, and maintain production-grade Python services and APIs that power high-impact machine learning platforms at enterprise scale
• Architect and implement high-throughput, low-latency distributed systems within Amazon Web Services (AWS) environments to support critical business workloads
• Build and manage scalable cloud-native applications leveraging AWS technologies including Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Managed Streaming for Apache Kafka (MSK), Simple Queue Service (SQS), and S3
• Develop reusable service frameworks, shared libraries, and modular application components that accelerate engineering delivery across teams
• Design and implement infrastructure-as-code solutions using Terraform and CloudFormation to enable repeatable, auditable, and scalable deployments
• Create and maintain monitoring, alerting, and observability solutions utilizing platforms such as Datadog, Dynatrace, and Splunk to ensure operational excellence
• Deploy and support applications in production environments while ensuring adherence to service-level objectives and service-level agreements
• Implement secure-by-design engineering practices, automated testing, and deployment strategies including blue/green and canary releases
• Review code, provide architectural guidance, and mentor engineers on software engineering best practices to elevate team capability
• Collaborate with product managers, platform engineering teams, and site reliability engineers to deliver scalable, business-aligned solutions
• Drive adoption of enterprise-approved AI-assisted engineering practices to improve code quality, operational excellence, troubleshooting, and delivery efficiency

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Advanced proficiency in Python programming, object-oriented design, and modular software architecture
• Experience building and operating large-scale, high-performance cloud-native services within AWS environments
• Hands-on experience with AWS technologies including EKS, ECS, MSK (Kafka), SQS, and S3
• Strong experience implementing infrastructure-as-code solutions using Terraform and/or CloudFormation
• Expertise in designing, deploying, and supporting distributed systems in production environments
• Experience with observability, monitoring, logging, and alerting platforms such as Datadog, Dynatrace, and Splunk
• Strong understanding of API design, microservices architecture, and scalable system design patterns
• Experience implementing automated testing, CI/CD pipelines, deployment automation, and secure software engineering practices
• Demonstrated experience utilizing approved AI-assisted software development tools for coding, code review, testing acceleration, troubleshooting, and operational support
• Strong understanding of responsible AI usage, application security, resiliency requirements, compliance standards, and mentoring engineers on engineering best practices

Preferred qualifications, capabilities, and skills

• Strong knowledge of distributed systems reliability patterns, including resiliency engineering, self-healing architectures, backpressure management, and idempotency
• Experience optimizing real-time and event-driven architectures at scale, particularly with Kafka-based messaging systems
• Experience implementing end-to-end observability, automated operational runbooks, and proactive monitoring frameworks
• Familiarity with CI/CD best practices, canary deployments, blue/green deployment strategies, and release automation within cloud environments
• Familiarity with Generative AI and Large Language Model technologies and experience building engineering solutions that leverage AI/LLM platforms