Senior Lead Security Engineer, AI

Job Summary As a Senior Lead AI Security Engineer in our Cybersecurity team, you will design and deliver secure artificial intelligence solutions that support critical cyber use cases. You will play a key role in shaping platform standards and governance, collaborating with cross‑functional teams, and driving innovation in secure AI. Together, we will build foundational capabilities and create lasting impact for our organization and the wider community. Job Responsibilities Lead end-to-end design and delivery of AI solutions for cyber use cases, from problem framing and data integration to model development, evaluation, deployment, and monitoring. Build secure LLM/RAG services and ML pipelines that integrate with SIEM/XDR, EDR, SOAR, IAM, ITSM, CMDB, code repos, and cloud telemetry. Establish engineering standards for secure AI: prompt security, tool/function calling patterns, input/output validation, PII masking, secrets handling, and deterministic fallbacks. Create evaluation harnesses with offline/online metrics, golden datasets, adversarial prompt sets, jailbreak tests, and safety/quality KPIs. Partner with platform teams to stand up reusable AI components: LLM gateways, vector stores, feature stores, evaluation/observability, and governance workflows. Implement drift and quality monitoring; define SLAs/SLOs; build incident response runbooks for AI-enabled services. Collaborate with risk and MRGR-style governance partners to meet documentation, validation, and attestations; maintain model/AT inventories, monitoring plans, and change logs. Deliver measurable impact: reduce MTTR, improve detection precision, automate control evidence collection, and accelerate secure engineering. Mentor engineers and analysts; publish playbooks, templates, and safe prompt libraries; lead brown‑bags and office hours for adoption. Drive a roadmap of 2–3 flagship capabilities per year (e.g., SOC triage assistant, controls automation agent, DevSecOps code copilot). Required Qualifications, Capabilities, and Skills Minimum 7 years of software/security engineering, including hands‑on experience in one or more of: detection engineering, SecOps, AppSec/DevSecOps, or cloud security. Minimum 3 years building and operating applied ML/LLM systems in production (RAG pipelines, embeddings, fine‑tuning/specialization, vector databases, model serving). Proficiency in Python and at least one of: Java, Scala, or TypeScript; experience with microservices, APIs, containers, and Kubernetes. Familiarity with SIEM, EDR, SOAR, IAM, and ITSM integrations; streaming/data engineering with Kafka or similar. Experience with LLM orchestration and guardrails (prompt engineering, injection defense, tool calling, safety filters). Hands‑on with ML #J-18808-Ljbffr