Senior Lead AI Security Engineer

JPMorganChase; Columbus (OH); On-site; USD 125,000 - 160,000; Full time

Job summary

JPMorganChase is seeking a Senior Lead AI Security Engineer to lead the design and delivery of secure AI solutions for critical cyber use cases. This role involves collaboration with cross-functional teams and shaping governance standards.

The ideal candidate will have a strong background in detection engineering and applied ML systems, alongside proficiency in Python and experience with security tools. The position is based in Columbus, Ohio, and offers a dynamic work environment.

Qualifications

• Minimum 7 years of experience in software/security engineering.
• Hands-on experience in detection engineering, SecOps, AppSec/DevSecOps, or cloud security.
• Minimum 3 years working with applied ML/LLM in production environments.

Responsibilities

• Lead the design and delivery of secure AI solutions for cybersecurity.
• Collaborate with teams to drive innovation in AI.
• Mentor engineers and analysts while improving secure engineering practices.

Skills

Detection engineering

Software engineering

Applied ML/LLM systems

Python

Java

Scala

TypeScript

Microservices

Kubernetes

Data protection

Tools

PyTorch

TensorFlow

scikit-learn

Kafka

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

• 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

• 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.

Equal Opportunity

We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.