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Tech Stack
Tools & technologiesAnsibleAWSAzureCloudGoGoogle Cloud PlatformITSMJavaJenkinsKubernetesPrometheusPythonServiceNowSplunkTerraform
About the role
Key responsibilities & impact- Implement and optimize our AI SRE Teammate to meet the needs of our customers in production and pre-production environments.
- Proactively monitor customer deployments (with Ciroos!) to ensure that our customers get the best out of the product.
- Use Ciroos to uncover latent reliability issues (misconfigurations, deployment regressions, scaling) of customer environments.
- Recommend best practices to customers for implementing the Ciroos AI SRE Teammate in their environment.
- Plan, design, build, and maintain highly scalable, reliable, and efficient infrastructure for our AI SRE Teammate.
- Serve as the customer’s technical advocate into engineering and product.
- Conduct post-incident reviews to identify root causes and implement preventative measures.
- Ensure security best practices are integrated into customer deployments.
- Train SRE, Ops, and Platform teams at the customer on how to operate with Ciroos AI SRE Teammate.
- Lead enterprise migrations onto Ciroos (e.g., replacing incumbent alerting/AIOps platforms such as BigPanda, Moogsoft, PagerDuty, Elastic, or Splunk), including replicating hundreds to thousands of existing correlation rules, running phased cutovers, and hitting hard customer go-live dates.
- Design, build, and tune alert normalization and correlation policies—writing conditions and operators (IN / CONTAINS / REGEX), extracting fields from noisy sources (email, Jenkins, CI/CD), and choosing tailored playbooks over catch-all rules.
- Integrate Ciroos with the customer’s operational stack—ticketing (e.g., ServiceNow bi-directional sync and field mapping), collaboration (Microsoft Teams / Slack), observability (Dynatrace, Quantum Metric, Databricks), and knowledge sources (GitHub, Confluence)—including any required app-marketplace certification.
- Validate and continuously improve investigation quality—diagnosing query-conversion and enrichment errors, unblocking failed investigations, and tuning RCA accuracy so the AI SRE Teammate earns customer trust.
- Stand up proactive health monitoring for customer deployments (e.g., real-time Slack status channels, SLA-driven alerts on missing metadata or ticket-creation failures) so issues are caught before the customer notices.
- Own customer-facing communications and delivery artifacts—weekly executive status updates, SLA documents, escalation paths, and migration/scope-change tracking—and coordinate daily syncs with customer project leads.
- Build long-term technical relationships with senior engineering leaders at customer organizations.
- Own customer implementations end-to-end—from technical discovery and solution design through production cutover, UAT, go-live, hypercare, and post-launch stabilization—driving execution independently across customer, product, and engineering threads, and demonstrating MTTR reduction and elimination of manual toil.
- Translate messy, ambiguous customer requirements into clear technical designs, milestones, trade-offs, and acceptance criteria, and keep execution truth legible by making blockers, risks, dependencies, and scope changes explicit early with clear owners and next steps.
- Design and ship AI-powered investigation and remediation workflows with clear tool boundaries, guardrails, deterministic fallbacks, and human-in-the-loop controls—and define quality controls (versioning, grounding, evals, feedback loops) so the AI SRE Teammate stays trustworthy in production.
- Ship reusable deployment modules, reference architectures, runbooks, and cutover plans (“golden paths”) that accelerate future customer work and raise delivery repeatability across accounts.
- Own the value case: set impact hypotheses, baselines, and KPIs (e.g., MTTI/MTTR reduction, toil eliminated), run pre-/post-deployment measurement, and report ROI to executive sponsors.
- Channel recurring field learnings into roadmap input and product hardening, especially where the same customer pain points show up across deployments.
- Contribute to a culture of continuous learning and improvement within Ciroos.
Requirements
What you’ll need- Customer obsessed: Deep empathy for customers in operational roles such as yours (SREs, ITOps, and DevOps) with a passion to reduce their toil and innovate on their behalf, and always willing to go the extra mile to delight customers.
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- At least 6 years of experience in Site Reliability Engineering, DevOps, or a similar operational role, including technical leadership or end-to-end ownership of customer-facing delivery. Prior experience in a technical customer success or forward-deployed/solutions engineering role is a strong plus.
- Strong proficiency in at least one programming language (e.g., Python, Go, Java).
- Extensive experience with cloud platforms (AWS, GCP, or Azure).
- Solid understanding of Kubernetes, infrastructure as code tools (e.g., Terraform, CloudFormation, Ansible), and CI/CD pipelines.
- Intimately familiar with Generative AI, ML concepts and technologies. You must be actively using AI tools to drive your own personal productivity.
- Strong hands-on experience with observability tools (e.g., Dynatrace, Datadog, Prometheus), ITSM/ticketing systems (e.g., ServiceNow), and incident management/response systems—including integrating and mapping data between them. Experience migrating a customer off an incumbent AIOps, alerting, or incident-management platform (e.g., BigPanda, Moogsoft, PagerDuty, Elastic, Splunk) is a strong plus.
- Excellent problem-solving and analytical skills to diagnose complex systems systematically, including comfort writing and debugging alert-correlation logic, normalization rules, and regular expressions against messy real-world alert data.
- Strong written and verbal communication skills with a collaborative mindset.
- Track record of independently owning enterprise implementations end-to-end—not just contributing tasks within a larger delivery team—including managing timelines and deliverables, driving daily/weekly customer syncs, and producing executive-facing status updates and SLA documentation, with strong customer outcomes and minimal execution drift.
- Strong integration and data experience: APIs, webhooks, event-driven systems, schema alignment, transformations, retries/idempotency, reliable error handling, and end-to-end integration configuration (field mapping, bi-directional sync, SSO/SAML)—including, where required, taking apps through marketplace certification (e.g., ServiceNow Store, Microsoft Teams).
- Hands-on experience designing and delivering production-grade AI/automation workflows, ideally LLM-powered, with practical guardrails and evaluation approaches.
- Comfort with enterprise security and architecture requirements—RBAC, encryption, auditability, private networking, identity, and data-handling expectations.
- Calm, high-judgment execution in ambiguous, fast-moving customer environments, moving fluidly between system-level architecture and execution-level detail.
- Intrinsically curious and able to thrive amid ambiguity, shifting customer scope, and hard deadlines—driving projects to completion while keeping enterprise stakeholders aligned.
Benefits
Comp & perks- Comprehensive medical, vision, and dental benefits.
- 401k plans and commuter benefits.
- Free lunches, snacks, and top-of-the-line espressos!
ATS Keywords
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Hard Skills & Tools
Site Reliability EngineeringCloud Platforms (AWS, GCP, Azure)Programming Languages (Python, Go, Java)Infrastructure as Code (Terraform, CloudFormation, Ansible)CI/CD PipelinesAlert Normalization and Correlation LogicIntegration and Data Experience (APIs, Webhooks)Incident Management SystemsAI Technologies (Generative AI, ML)Security Best Practices
Soft Skills
Customer ObsessionProblem-SolvingAnalytical SkillsStrong Communication SkillsCollaboration Mindset