What are the responsibilities and job description for the Lead Software Engineer - DevOps / Full-Stack / MLOps position at JPMorgan Chase?
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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