Job Description
AgentOps - Pipelines, Environments and Reliability
As part of our client's engineering organization, we are looking for Agentic DevOps Engineers to make delivery and operations automatic inside our pods. This is the largest engineering population in the model, with positions across the platform pods and the Data Accelerators pod.
The successful candidate builds the pipelines, environments and observability that let a pod of five release continuously and run what it builds, and wires AI agents into operations so that routine triage handles itself.
This role is distinct from the Senior Platform Engineer. The DevOps Engineer owns a single pod's pipelines, environments and runtime. The Platform Engineer builds the shared paved road those pods consume. Candidates should be clear which they are applying for.
Key Responsibilities:
- Build and run the pod's delivery pipelines in GitHub Actions, with environments managed through GitOps.
- Automate environment provisioning and infrastructure as code so environments are reproducible rather than nursed.
- Stand up observability and wire agent-assisted triage so routine alerts are diagnosed, and often remediated, without a human.
- Own reliability for the pod's services: service level objectives, error budgets and incident response for genuine escalations.
- Eliminate manual deployments and manual first and second line triage.
- Harden the supply chain with secret scanning, dependency hygiene and image hygiene enforced in the pipeline.
- Automate provisioning and governance across the data platforms the pod operates.
- Contribute application and platform code, not only pipeline configuration.
About You:
The ideal candidate is an engineer who automates their way out of operational work and treats a manual runbook step as a defect. They are comfortable debugging a failing pipeline, a misconfigured cluster and an application bug in the same day, and they would rather teach an agent to triage an alert than triage it twice. They are not a release coordinator or a ticket-driven operator. They own the delivery and runtime path for a pod that ships continuously.
Requirements
- Professional DevOps, SRE or platform engineering experience running production workloads.
- Strong CI/CD skills, with GitHub Actions preferred, including pipeline design for multiple environments.
- Infrastructure-as-code experience with Terraform or Bicep.
- Containers and orchestration with Docker and Kubernetes.
- Strong experience designing and operating solutions on Microsoft Azure.
- Knowledge of Azure networking, identity and access management, storage, monitoring and governance controls.
- Experience working within large-scale enterprise cloud environments.
- Scripting and development ability in Python, Java, Go, Bash or PowerShell.
- Ability to build maintainable automation rather than accumulating scripts.
- Experience with API integrations between platform services.
- Experience defining and operating against service level objectives.
- Observability tooling such as Grafana, Prometheus or Azure Application Insights.
- Incident response experience, including writing the follow-up that prevents a repeat.
- Strong GitHub experience including Actions, repository management, branching strategies, pull request workflows and automation.
- Experience working in Git-based development models across multiple teams.
- Practical experience directing AI coding agents such as Factory.ai or GitHub Copilot to produce production code, and reviewing that output critically rather than accepting it.
- Ability to write precise, testable specifications that an agent can build from without coming back for clarification.
- Sound judgment about where agent autonomy is appropriate and where a human decision is mandatory.
- Comfort being measured on agent autonomy coverage and rework rate alongside conventional delivery measures.
- Preferred: GitOps tooling such as Argo CD or Flux.
- Preferred: Internal developer platforms such as Nullplatform.
- Preferred: Databricks, Snowflake, MongoDB Atlas or Microsoft Fabric platform operations.
- Preferred: DORA metrics and policy as code.
- Preferred: GitHub Advanced Security.
- Preferred: Experience in global, distributed engineering organizations.
Benefits
- Pods of five with end-to-end ownership. Your pod builds, tests, releases and runs its own slice of the platform. Nothing is handed to a separate QA or operations function.
- Kanban and flow rather than sprints. Work is pulled, work in progress is limited, and throughput, lead time and cycle time are measured. There is no sprint commitment ceremony and no PI planning.
- A six-phase product lifecycle: Plan, Define, Build, Review, Release and Support.
- Tooling: GitHub for Projects, Issues, Actions and GitOps; Factory.ai and GitHub Copilot for agentic engineering; Nullplatform for environments; Aha for portfolio intake.
- Agent-first delivery. We target 60 to 70 percent agent autonomy on eligible work and rework below 15 percent, which shifts engineering effort from typing code to specifying and reviewing it.