The Role

At NICE, we don't limit our challenges. We challenge our limits. Always. We're ambitious. We're game changers. And we play to win. We set the highest standards and execute beyond them. And if you're like us, we can offer you the ultimate career opportunity that will light a fire within you.

We are looking for a Senior AI Software Engineer who thrives at the intersection of infrastructure, DevOps, and applied AI. In this role you will design and build intelligent automation systems that reduce toil, accelerate delivery, and improve platform resilience for teams spanning SRE, DevOps, Systems, Networking, and Voice Engineering across the NICE CXone global multi-region cloud platform, including sovereign regions for UK Gov, AU Gov, EU, and FedRAMP. Your code will run in production and will directly impact how hundreds of engineers build, deploy, and operate mission-critical software.

What You'll Own

Infrastructure Automation
• Design and build end-to-end automation pipelines for cluster provisioning and new region and environment builds, including AWS account bootstrapping for sovereign cloud regions such as UK, AU, EU, and FedRAMP.
• Automate infrastructure lifecycle tasks that currently require manual navigation of web-based tooling by translating operator workflows into reliable, idempotent, API-driven pipelines.
• Build tooling to provision and validate monitoring, alerting, telemetry pipelines, and network circuits for new regions at launch.
• Integrate automation into existing CI/CD systems including GitLab CI and Backstage, with proper pipeline gates and exit-criteria-driven release controls.

AI-Driven Operations
• Contribute to the AI Ops Incident Intelligence Platform by building agents and orchestration layers that surface meaningful signal from noise during incidents, identify recurring failure patterns, and recommend remediation paths.
• Develop AI-powered change risk scoring and production readiness gates that integrate directly into the deployment pipeline.
• Build automated rollback capabilities triggered by business-level signals such as error rate, latency, and revenue impact, rather than simple health checks alone.
• Work within the DevOps AI Working Group to coordinate AI pipeline efforts across SRE, DevOps, Systems, and Networking teams, ensuring shared standards and preventing fragmented shadow-ops tooling.

Cross-Functional Engineering
• Collaborate directly with SRE, Systems, Network, and Voice Engineering teams to identify high-toil workflows and translate them into automation candidates.
• Build and maintain Model Context Protocol (MCP) server integrations that allow AI agents to safely interact with operational tooling including ServiceNow, Jira, Confluence, and AWS APIs, with appropriate guardrails in place.
• Design all solutions with sovereign isolation requirements in mind, ensuring that UK, AU, and EU sovereign instances operate independently and within compliant data boundaries.

Qualifications
• 5 or more years of software engineering experience with a strong emphasis on backend systems, infrastructure automation, or platform engineering.
• Production-grade experience in Python or Go, with comfort working in polyglot environments. Experience with TypeScript or Node.js is a plus.
• Deep experience with AWS, including IAM, EC2, EKS, RDS, Lambda, CloudWatch, and multi-account architectures.
• Hands-on experience building automation that interacts directly with REST or GraphQL APIs, not simply writing scripts that call CLI tools.
• Familiarity with CI/CD systems such as GitLab CI or GitHub Actions, and with infrastructure-as-code tooling such as Terraform, CDK, or Pulumi.
• Demonstrated ability to build AI-powered workflows using LLM APIs including Anthropic, OpenAI, or equivalent, covering prompt engineering, tool use and function calling, and agentic orchestration patterns.
• Strong understanding of distributed systems, Kubernetes, and cloud-native observability including metrics, logs, and traces.
• Excellent written and verbal communication skills, with the ability to translate complex systems into clear documentation, runbooks, and architecture diagrams.
• A bachelor's degree in Computer Science, Engineering, or a related field, or equivalent work experience.

Bonus Experience
• Experience with agentic AI frameworks such as LangGraph, CrewAI, AutoGen or AG2, Semantic Kernel, or Smolagents.
• Prior work in regulated or sovereign cloud environments such as FedRAMP, IL4, UK G-Cloud, or AU PROTECTED.
• Familiarity with Backstage developer portals and their integration with CI/CD pipelines.
• Experience with OpenObserve, Grafana, or similar observability platforms at scale.
• Background in SRE, NOC tooling, or incident management automation, especially alert correlation and runbook automation.
• Exposure to Kafka or other distributed messaging systems in a production operations context.
• Prior experience building internal tooling that replaced manual, click-through web interfaces for infrastructure operations.

What Success Looks Like

30 Days

You have completed a deep dive into cluster build and region provisioning workflows and identified the top three automation gaps. You have delivered a first working prototype that automates at least one currently manual step.

60 Days

An end-to-end automation pipeline has shipped for at least one cluster or region build workflow. You are contributing to the DevOps AI Working Group with a clear ownership lane, and an MCP server integration is scoped and in active development.

90 Days

There is a measurable reduction in manual effort for at least one team. AI-driven automation is in active use by SRE or DevOps engineers, and you are established as a trusted collaborator across SRE, Systems, Network, and Voice teams.