Corporate Vice President -AI/ML Security Operations Engineer

Location Designation: Hybrid - 3 days per week The AI / ML Security Operations Engineer is a senior, hands‑on engineering role embedded within the Application Security organization. The role secures New York Life’s machine learning and AI pipelines as they evolve from isolated experiments into production, agentic, and automated decisioning systems. The engineer sits at the intersection of ML engineering, platform engineering, and security, and is accountable for establishing controls, guardrails, and reference patterns that scale with AI adoption across the enterprise. The engineer is responsible for securing the full ML lifecycle—from data ingestion and feature pipelines through model training, registry, deployment, and execution—with a primary focus on Google Cloud Vertex AI as the enterprise ML platform. Day‑to‑day work includes building guardrails for agentic and tool‑invoking AI use cases, protecting ML supply‑chain integrity, integrating ML security controls into existing AppSec CI/CD and SSDLC processes, contributing security requirements to ML platform and identity decisions owned by partner teams, and partnering directly with data scientists, ML engineers, and platform owners to operationalize secure‑by‑default patterns. The role is a senior individual contributor with strong cross‑functional influence expectations; it requires hands‑on expertise in a regulated environment and the ability to define enterprise standards, mentor peers, and engage credibly with risk, audit, and model risk management stakeholders. What You’ll Bring Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience, with 5+ years in application security, cloud security, or security engineering. Hands‑on production experience securing at least one major ML platform; Vertex AI strongly preferred, SageMaker or Azure ML acceptable as transferable experience. Strong working knowledge of the end‑to‑end ML lifecycle and MLOps workflows: data ingestion, feature pipelines, training jobs, model registry, deployment patterns, and online/offline serving. Practical understanding of how ML environments should be separated across dev, training, staging, and production, and the ability to partner with platform teams to ensure those boundaries hold from a security standpoint. Working knowledge of non‑human identities, service accounts, workload identity federation, and automated CI/CD or pipeline‑driven workflows, with the ability to evaluate whether identity patterns proposed by partner teams meet security requirements. Fluency with AI/ML‑specific threat scenarios—including data poisoning, model theft, training data exfiltration, inference abuse, prompt injection, indirect prompt injection, unsafe tool invocation, and agentic misuse—and the ability to translate them into concrete controls. Hands‑on experience integrating security controls into CI/CD pipelines and infrastructure‑as‑code environments (Terraform, GitHub Actions, GitLab CI, Cloud Build, or equivalent). Working understanding of cloud IAM principles and least‑privilege design, sufficient to review and provide security input on identity patterns owned by platform and cloud teams. Application security fundamentals: authentication/authorization patterns, supply chain security (SLSA, SBOMs, signed artifacts), secure API design, and secrets management. Proficiency in Python for automation, security tooling, and detection logic; candidates should be able to walk through code they have personally written, not just reviewed. Ability to operate as both a hands‑on engineer and a pattern‑setter, comfortable building the first instance of a control and then turning it into a reusable enterprise standard. Preferred Qualifications Direct experience securing agentic AI systems, orchestration frameworks (LangChain, LangGraph, Vertex AI Agent Builder, ADK, CrewAI), or autonomous tool‑invoking workflows in production. Working familiarity with AI security frameworks such as MITRE ATLAS, OWASP LLM Top 10, OWASP ML Top 10, NIST AI RMF, Google Secure AI Framework (SAIF), or Databricks AI Security Framework. Experience designing governance models for ML platforms in regulated industries (financial services, healthcare, etc.), including how controls map to model risk management (SR 11‑7) and applicable audit requirements. Background working alongside data scientists and ML engineers on production model deployments, not just reviewing their work from a security distance. Exposure to model risk management, model validation, or model controls partnerships with second‑line risk functions. Experience with policy‑as‑code and guardrail enforcement at scale (OPA / Rego, Cloud Custodian, Conftest, Sentinel, or equivalent). Familiarity with detection engineering for ML workloads, including log sources from Vertex AI, model serving endpoints, agent execution traces, and how to write meaningful detections against them. Hands‑on exposure to LLM gateways, content safety and guardrail products (Lakera, Protect AI, NeMo Guardrails, Llama Guard, Vertex AI Safety Filters), or self‑built equivalents. Pay Transparency Salary Range: $147,500–$211,000 Overtime eligible: Exempt Discretionary bonus eligible: Yes Sales bonus eligible: No Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Employees are also eligible for an annual discretionary bonus. Job Requisition ID: 94174 #J-18808-Ljbffr New York Life