Senior Lead AI Security Engineer

JPMorgan Chase & Co.; Columbus (OH); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading financial services company seeks a Senior Lead AI Security Engineer for its Cybersecurity team. The role involves designing secure AI solutions for critical cyber use cases and collaborating with various teams to drive innovation. Candidates need at least 7 years of software/security engineering experience and 3 years working with applied ML/LLM systems. Responsibilities include delivering AI solutions, establishing security standards, and mentoring others. This position offers an opportunity to create a lasting impact within the organization.

Qualifications

• 7+ years of software/security engineering experience.
• 3+ years building and operating ML/LLM systems in production.
• Strong understanding of secure SDLC, privacy, and data protection.

Responsibilities

• Lead design and delivery of AI solutions for cyber use cases.
• Build secure AI/ML pipelines and services.
• Establish engineering standards for secure AI.

Skills

Detection engineering

AppSec/DevSecOps

Cloud security

Proficiency in Python

Microservices

APIs

Kubernetes

Streaming/data engineering

Prompt engineering

ML ecosystem knowledge

Tools

PyTorch

TensorFlow

Kafka

LangChain

Ray

Job description

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.