Production Support Engineer - Enterprise Platforms

FIS•Atlanta, GA

3d•$144,730 - $246,040•Hybrid

About The Position

The Production Support Engineer is a level 2, hands-on, customer-facing role within a newly acquired startup building an AI-enabled autonomous banking platform for Tier-1 financial institutions. The engineer takes support tickets directly from Tier-1 banks, investigates and reproduces issues, provides clear evidence to the owning engineering team, and keeps customers informed throughout the investigation. The role connects application behavior, Kubernetes infrastructure, logs, metrics, and customer impact. It is a production support role—not an SRE, DevOps, architecture, or platform engineering position—and does not own or build infrastructure. It offers opportunities to work on projects with platform engineering and a potential path into platform engineering, typically over one to two years. The role supports Kubernetes-based banking platform environments across cloud, hybrid, and customer-hosted deployments. The position is hybrid, with three days onsite at FIS offices in Jacksonville, Florida; Milwaukee, Wisconsin; Atlanta, Georgia; or New York City, New York. Current and future sponsorship is unavailable. The position has direct impact on customer trust, production reliability, and operational continuity for Tier-1 banking institutions, with visibility across customer, support, engineering, platform, and product teams. The full-time salary range is $144,730 to $246,040; individual starting pay depends on factors including job-related skills, experience, education or training, and work location.

Requirements

Strong hands-on troubleshooting experience in production or customer-facing technical environments.
Working knowledge of Kubernetes, including pods, services, traffic flow into and within clusters, and storage; hands-on troubleshooting with kubectl is required. Building or running clusters is not required.
Solid Linux troubleshooting skills, including processes, memory, disk space, and inodes.
Strong understanding of networking fundamentals, APIs, services, and distributed-system behavior.
Basic understanding of PostgreSQL or another relational database, including replication, primary/replica, and active-passive setups; deep database administration is not required.
Ability to read, interpret, and correlate logs, metrics, traces, and alerts; comfort using Grafana dashboards, running metric queries, and following traces. Building observability tools is not required.
At least one proven customer-facing technical role.
Experience working directly with customers during investigations, escalations, or production support scenarios.
Strong ownership mindset and bias toward resolution.
Practical, hands-on problem-solving approach and interest in investigation and troubleshooting.
Ability to work as a peer within a small team.
Ability and willingness to integrate LLMs and AI tooling into daily investigation and support work.

Nice To Haves

Experience supporting enterprise SaaS, banking platforms, fintech systems, or regulated infrastructure.
Familiarity with cloud or hybrid environments such as AWS, GCP, Azure, or on-premises enterprise deployments.
Exposure to deployment workflows, Helm, Terraform, Flux, ArgoCD, Kafka, or distributed databases.
Awareness of Kubernetes networking plugins (CNI), such as Cilium.
Deployment ownership experience.

Responsibilities

Investigate and resolve complex technical issues across application, infrastructure, and platform layers.
Analyze logs, metrics, traces, and alerts to identify root causes and correlate symptoms across systems.
Troubleshoot Kubernetes and container issues involving pods, services, deployments, networking, and resources.
Reproduce issues and hand them to the owning engineering team with logs, traces, timestamps, and reproduction steps.
Engage directly with enterprise banking customers to understand, investigate, and resolve technical issues.
Acknowledge new issues quickly and provide regular customer updates while investigations are open, including when there is no new information.
Communicate findings clearly to technical and non-technical customer stakeholders.
Use observability tools such as Prometheus, Grafana, Loki, OpenTelemetry, Datadog, Splunk, or similar tools to investigate system behavior.
Work with the platform’s AI-driven SRE capabilities, validate incident insights and AI-based root-cause findings, and act on them, including in relation to auto-remediation.
Improve runbooks, dashboards, alerts, and support processes based on recurring issues and lessons learned.
Support Kubernetes-based banking platform environments across cloud, hybrid, and customer-hosted deployments.
Assist with deployment-related troubleshooting when required.
Contribute to projects alongside the platform engineering team during quieter periods.