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Sr Mgr Software Engineering

Job in Norcross, Gwinnett County, Georgia, 30003, USA
Listing for: ACI Worldwide
Full Time position
Listed on 2026-07-14
Job specializations:
  • Software Development
    DevOps
Salary/Wage Range or Industry Benchmark: 170000 - 240000 USD Yearly USD 170000.00 240000.00 YEAR
Job Description & How to Apply Below

Powering the world’s payments ecosystem – ACI powers the payments ecosystem globally, and you power ACI. You’ll innovate, collaborate, and grow in an energetic technology culture with decades of proven success. ACIers – in all roles and levels – are truly your colleagues, and many are your friends. Our size and reach allow you to see the global impact of your work.

You are visible, your talents are valued, and you are empowered to shape the future of payments.

Role Summary

We are seeking a hands‑on Sr. Engineering Manager to lead a cross‑functional team (Software Engineering, Dev Ops/Platform, Support and Quality Engineering) building and operating a low‑latency, high‑throughput core banking product platform. You will be accountable for end‑to‑end delivery: architecture-to‑production execution, operational excellence, reliability, security/compliance, and an automation‑first quality strategy. You will also bring demonstrated experience delivering AI‑enabled initiatives and implementing AI‑based solutions in production.

Location & Work Model:
This is a hybrid position based out of Norcross, GA. Candidates are expected to be on‑site on designated days and may work remotely on other days in accordance with team and business needs.

Key Responsibilities
  • Engineering Leadership & Delivery
    • Lead and grow a cross‑functional team spanning backend, frontend, Dev Ops/Platform engineering, and Quality Engineering; drive strong ownership, execution discipline, and continuous improvement.
    • Own delivery for roadmap initiatives and platform modernization across the full SDLC: planning, design, implementation, reviews, testing, release, and production operations.
    • Establish and communicate technical direction in partnership with Product Management and Architecture to ensure the platform meets business goals and non‑functional requirements.
  • Low‑Latency, Reliability, and Performance (NFR‑First)
    • Drive performance and latency engineering: define latency/throughput SLOs, performance budgets, regression testing, and capacity planning to maintain predictable low‑latency behavior under load.
    • Build “always‑on” operational readiness: observability (logs/metrics/traces), runbooks, safe rollouts (feature flags, progressive delivery), and rapid rollback practices.
    • Partner with Platform/SRE functions to ensure Kubernetes runtime reliability patterns (HA, scaling, and operational tooling) are built into the product delivery lifecycle.
  • Dev Ops / Platform Engineering Ownership
    • Lead the team’s cloud‑native delivery model on Kubernetes (including Git Ops‑based deployment and configuration‑as‑code).
    • Own and evolve CI/CD and artifact practices using technologies such as Ansible, Nexus, Carvel toolchain (e.g., ytt, kapp‑controller), and Kubernetes‑native release workflows.
    • Ensure secure, repeatable environment provisioning and reliable deployments across dev/test/stage/prod with automation as the default.
  • Quality Engineering & Test Automation (Shift‑Left)
    • Build a “quality‑first” culture with an automated testing strategy across unit/integration/e2e, with strong coverage targets and non‑negotiable quality gates.
    • Implement and operationalize modern test orchestration and automation tooling, including Testkube, and enforce reliable, maintainable CI test execution.
    • Drive security and compliance testing as part of CI/CD (SAST/DAST/dependency scanning), ensuring regulated‑workload readiness and auditability.
  • AI‑Enabled Engineering & Delivered AI Projects (Must‑Have)
    • Lead delivery of AI‑enabled initiatives in two dimensions:
      • AI‑accelerated SDLC (planning, code review, test generation, documentation, defect reduction)
      • AI/ML/GenAI solution delivery where AI is part of the shipped product or operational capability
    • Establish responsible AI usage practices: teams are encouraged to use AI, but remain accountable for outputs; validate correctness, security, and maintainability before merging.
    • Enforce safe prompting and data handling: never place secrets/credentials/PCI/PII into prompts; use placeholders and abstractions.
  • People Leadership & Stakeholder Management
    • Coach engineers, Dev Ops, and QE leads; build career plans, performance feedback loops, and hiring strategies…
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