Senior Engineer, Anomali

• 5+ years building evaluation or testing infrastructure for ML/LLM systems or large-scale distributes systems
• Proven ability to translate product requirements into measurable metrics and test plans
• Strong Python skills (or similar language) and experience with modern data tooling
• Hands-on experience running A/B tests, canaries, or experiment frameworks
• Experience defining and maintaining operational reliability metrics (SLIs/SLOs) for AI-driven systems
• Familiarity with large-scale distributed or streaming systems serving AI/agent workflows (millions of events or alerts/day)
• Excellent communication skills -- able to clearly convey technical results and trade-offs to engineer, PMs, and analysts

Desirable

• We're looking for a Senior Engineer, AI Evaluation & Reliability to lead the design and execution of evaluation, quality assurance, and release gating for our agentic AI features
• You'll develop the pipelines, datasets, and dashboards that measure and improve agent performance across real-world SOC workflows -- ensuring every release is safe, reliable, efficient, and production-ready
• You will guarantee that our agentic AI features operate at full production scale, ingesting and active on millions of SOC alerts per day, with measurable impact on analyst productivity and risk mitigation
• This role partners closely with the Product team to deliver operational excellence and trust in every AI-drive capability
• Define quality metrics: Translate SOC use cases into measurable KPI's (e.g., precision/recall, MTTR, false-positive rate, step success, latency/cost budgets)
• Build continuous evaluations: Develop offine/online evaluation pipelines, regression suites, and A/B or canary test; integrate them into CI/CD for release gating
• Curate and manage datasets: Maintain gold-standard datasets and red-team scenarios; establish data governance and drift monitoring practices
• Ensure safety, reliability, and explainability: Partner with Platform and Security Research to encode guardrails, policy enforcement, and runtime safety checks
• Expand adversarial test coverage (prompt injection, data exfiltration, abuse scenarios)
• Ensure explainability and auditability of agent decisions, maintaining traceability and compliance of AI-driven workflows
• Production reliability & observability: Monitor and maintain reliability of agentic AI features post-release -- define and uphold SLIs/SLOs, establish alerting and rollback strategies, and conduct incident post-mortems
• Design and implement infrastructure to scale evaluation and production pipelines for real-time SOC workflows across cloud environments
• Drive agentic system engineering: Experiment with multi-agent systems, tool-using language models, retrieval-augmented workflows, and prompt orchestration
• Manage model and prompt lifecycle -- track version, rollout strategies, and fallbacks; measure impact through statistically sound experiments
• Collaborate cross-functionally: Work with Product, UX and Engineering to prioritize high-leverage improvements, resolve regressions quickly, and advance overall system reliability