Lead Software Engineer - DevOps / Full-Stack / MLOps

Testing Site - JPMC; Columbus (OH); On-site; USD 150,000 - 190,000; Full time

Job summary

JPMorganChase is seeking a Lead Software Engineer to join the Consumer & Community Banking Digital Cloud team in Columbus, OH. You will be a core technical contributor, driving secure, scalable architectures and automation across multi-functional areas, including ML platform design and model lifecycle management.

You will lead DevSecOps practices, implement Kubernetes-based deployments, and collaborate with vendors and internal teams to advance architectural outcomes and reliability across

Qualifications

• Formal training or certification in software engineering concepts and 5+ years applied experience.
• 8+ years of hands-on software/platform engineering experience, including leading cloud-native delivery for business-critical systems.
• Expert Infrastructure as Code with Terraform (modules, state backends, workspaces, CI integration, policy controls).
• Expert proficiency in Python for platform automation, tooling, and systems scripting; Bash/YAML/Helm familiarity.
• Deep experience with CI/CD (Jenkins, Spinnaker/Argo), artifact management, and automated testing strategies.
• Strong AWS/public cloud knowledge (VPC, ALB/NLB, ECR/EKS, IAM, KMS, CloudWatch/CloudTrail) and cloud networking basics.
• Experience with MLOps tools/platforms (MLflow, SageMaker, Vertex AI, Databricks, BentoML, KServe, Kubeflow).
• Understanding of data versioning and ML model lifecycle management; practical AI/LLM in DevSecOps use cases.

Responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting beyond routine approaches.
• Develops secure high-quality production code, and reviews and debugs code written by others.
• Identifies opportunities to automate remediation of recurring issues to improve stability.
• Leads evaluation sessions with vendors and internal teams to probe architectural designs and applicability.
• Design and develop a scalable ML platform to support model training, deployment, and monitoring.
• Build and maintain infrastructure for automated ML pipelines with reliable reproducibility.
• Set up monitoring and reliability for infrastructure and models using Prometheus and Grafana.
• Code infrastructure with Terraform and utilize Python for automation.
• Perform DevOps in Kubernetes, Docker, Helm, GitOps, and CI/CD pipelines (Jenkins, GitLab CI).

Skills

Terraform

Python

Kubernetes

CI/CD

AWS Cloud

Prometheus Grafana

LLM DevSecOps

Automation scripting

Education

Formal training or certification in software engineering

Tools

Docker

Helm

GitOps

Spinnaker/Argo

Jenkins/GitLab CI

MLflow

SageMaker VertexAI Databricks

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Digital Cloud team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Develops secure high-quality production code, and reviews and debugs code written by others
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes‑oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
• Design and develop a scalable ML platform to support model training, deployment, and monitoring
• Build and maintain infrastructure for automated ML pipelines, ensuring reliability and reproducibility supporting different model frameworks and architectures
• Set up monitoring and reliability for both infrastructure and models utilizing Prometheus and Grafana
• Code infrastructure with Terraform and utilizing Python for automation
• Perform DevOps in Kubernetes (K8s), Docker, Helm, GitOps, and CI/CD pipelines (Jenkins, GitLab CI)

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 8+ years of hands‑on software/platform engineering experience, including leading cloud‑native delivery for business‑critical systems.
• Expert Infrastructure as Code with Terraform (modules, state backends, workspaces, CI integration, policy controls).
• Expert proficiency in Python for platform automation, tooling, and systems scripting; familiarity with Bash/YAML/Helm.
• Deep experience with CI/CD (e.g., Jenkins, Spinnaker/Argo), artifact management, and automated testing strategies.
• Strong AWS/public cloud knowledge (VPC, ALB/NLB, ECR/EKS, IAM, KMS, CloudWatch/CloudTrail) and cloud networking fundamentals.
• Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow)
• Understanding of data versioning and ML models lifecycle management
• Practical experience applying agentic AI/LLM capabilities to DevSecOps use cases (e.g., assisted troubleshooting, code/IaC generation with review, runbook automation) with attention to accuracy, guardrails, and auditability.
• Containerization & DevOps:Expert skills in Kubernetes (K8s), Docker, Helm, GitOps, and CI/CD pipelines (Jenkins, GitLab CI)
• Monitoring & Reliability:Experience setting up monitoring for both infrastructure and models (drift detection, model accuracy) using Prometheus/Grafana.

Preferred qualifications, capabilities, and skills

• Experience deploying models using Canary, Blue/Green, or Shadow deployment strategies
• Previous experience deploying & managing ML models is beneficial
• Experience working in a highly regulated environment or industry
• Strong knowledge of AWS, Azure, or GCP, including serverless architectures, storage solutions, and network configuration.
• Postgres experience