Senior Lead Security Engineer, AI

JPMorgan Chase & Co.; Columbus (OH); On-site; USD 180,000 - 230,000; Full time

Job summary

JPMorgan Chase & Co. seeks a Senior Lead AI Security Engineer to design and deliver secure AI solutions for cyber use cases. You will shape platform standards, collaborate across teams, and drive innovation in secure AI, building reusable components and governance workflows.

Responsibilities include end-to-end AI system design, secure LLM/RAG services, and implementing monitoring, incident response, and compliance with risk governance. You will mentor teams and help publish safety playbooks.

Qualifications

• Minimum 7 years of software/security engineering experience with hands-on security and engineering.
• Minimum 3 years building and operating applied ML/LLM systems in production (RAG pipelines, embeddings, fine-tuning/specialization).
• Proficiency in Python and one of Java/Scala/TypeScript; experience with microservices, APIs, containers, and Kubernetes.
• Familiarity with SIEM, EDR, SOAR, IAM, and ITSM integrations; streaming/data engineering with Kafka.
• Experience with LLM orchestration and guardrails (prompt engineering, tool calls, 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 for documentation and monitoring.

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 governance partners to meet documentation, validation, and attestations; maintain model inventories 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 flagship capabilities per year (e.g., SOC triage assistant, controls automation agent, DevSecOps code copilot).

Skills

Python

Java

Scala

TypeScript

Kubernetes

APIs

Microservices

CI/CD

ML/LLM systems

PyTorch

LangChain

LlamaIndex

Vector stores

Security engineering

Data streaming

Tools

Kafka

Docker

ONNX

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.

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