Role Summary
Do you want to operate at the edge of innovation-solving mission-critical customer problems, shaping product direction, and deploying AI capabilities where they matter most? If you thrive in fast-moving environments where deep engineering meets real-world customer impact, this role awaits you!
We're looking for an experienced and curious Senior Forward Deployed Engineer (FDE) to join our team. You will serve as the technical vanguard for our largest and most sophisticated customers - deploying Ciroos in enterprise ecosystems, solving real-world production engineering bottlenecks, and translating operational pain into product leverage. This is a customer-facing engineering role where you directly integrate Ciroos into complex, heterogeneous enterprise environments. Your work materially influences our roadmap, accelerates customer adoption, and ensures successful production outcomes. You'll work closely with our product and engineering teams, and our customers, to ensure our AI SRE Teammate delights our customers. You'll be responsible for the availability, operational performance, emergency response, and support for our service that is currently deployed in multiple enterprise production environments. This is an opportunity to be part of a team that is defining a red-hot category (AI in SRE) led by a stellar team with an impeccable track record.
Responsibilities
• 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.
Qualifications
• 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.
Bonus Points
• Experience with operating/supporting customers running AI infrastructure or AI tools.
• Familiarity with incumbent alerting/AIOps and incident-management platforms (e.g., BigPanda, Moogsoft, PagerDuty, Elastic, Splunk), and the realities of migrating correlation rules and operational workflows off them.
• Experience building lightweight internal tooling and automation (Python/Node/bash) to accelerate validation, migration, and monitoring.
What Success Looks Like (First 6-12 Months)
In your first 6-12 months, success in this role looks like:
• You've owned one or more enterprise deployments end-to-end-discovery through go-live and hypercare-with strong customer outcomes and minimal execution drift.
• Your customers trust you, know what to expect, and are operating the Ciroos AI SRE Teammate confidently on their own.
• Risks, blockers, dependencies, and scope changes stay explicit and current across your accounts-no surprises reach the customer without a plan.
• You've demonstrably reduced customer toil and incident time (e.g., measurable MTTI/MTTR improvement) and reported it against a clear baseline.
• You've improved delivery repeatability by shipping at least one reusable pattern, reference architecture, or runbook the wider team now uses.
• You've turned recurring field pain into concrete product feedback that shaped the roadmap or hardened the platform.
