Lead Software Engineer - DevOps/Cloud

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 JPMorgan Chase within the Consumer and Community Banking, 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

Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies

Adds to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

8+ years of hands on software/platform engineering experience, including leading cloud native delivery for business critical systems.

Strong Kubernetes expertise (workloads, networking, security, autoscaling, upgrades, troubleshooting); experience operating clusters in production.

Expert Infrastructure as Code with Terraform (modules, state backends, workspaces, CI integration, policy controls).

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.

Solid understanding of SDLC and agile practices; champions secure coding, resiliency patterns, and release engineering.

Observability at scale: Prometheus/Grafana, datadog, log aggregation (e.g., Splunk), actionable SLO/SLA monitoring.

Demonstrated reduction of operational toil through automation and SRE practices (incident response, blameless postmortems, remediation).

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.

Excellent communication and leadership skills; ability to influence architecture and mentor engineers across teams.

Preferred qualifications, capabilities, and skills

Programming & Scripting: Expert-level Python is mandatory, along with proficiency in Bash, Java, or C++ for building automation scripts.

ML Frameworks & Tools: Hands-on experience with TensorFlow, PyTorch, or Scikit-learn, plus MLOps tools like MLflow, Kubeflow, Vertex AI, or DVC.

Infrastructure & Cloud: Strong knowledge of AWS, Azure, or GCP, including serverless architectures, storage solutions, and network configuration.

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.

Database Systems: Proficiency in managing SQL/NoSQL databases to handle data for training and inference.