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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/LLM ecosystems: PyTorch or TensorFlow; scikit-learn; LangChain/LlamaIndex; ONNX/Triton/Ray
• Strong understanding of secure SDLC, privacy, and data protection; ability to partner with governance to meet documentation and monitoring requirements.
• Demonstrated ability to ship secure, reliable AI features with clear metrics and post-deployment monitoring.

Preferred qualifications, capabilities and skills

• Experience building developer copilots for AppSec/DevSecOps (IaC scanning, secrets detection, SAST/DAST triage).
• Cloud security engineering across one or more major providers; IaC and policy-as-code.
• Experience or exposure to Cyber operations, Adversarial ML and LLM red teaming experience (prompt injection, data exfiltration, model abuse, poisoning defenses).
• Graph ML for identity/threat detection; anomaly detection over telemetry.
• GPU optimization, model quantization/distillation, and on-prem/private model deployment.
• Familiarity with governance for AI/ML systems in regulated environments.

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