Senior Security Engineer - AI

MUFG•Jersey City, NJ

1d•$140,000 - $203,000•Hybrid

About The Position

MUFG is seeking a highly motivated Security Engineer to design, develop, and deploy autonomous agents that eliminate manual overhead and drive intelligent decision-making across the organization while ensuring proper guardrails and security controls are implemented. The selected colleague will work at an MUFG office or client sites four days per week and work remotely one day. A member of our recruitment team will provide more details.

Requirements

Expert-level in Python with hands-on in data ingestion, cleaning, machine learning libraries covering Scikit-Learn, Pytorch, system and web-based integrations.
Proficiency with LangChain, CrewAI, or AutoGPT for agent orchestration.
Hands-on experience in using and tuning Machine Learning models for wide range of cyber security use cases.
Hands-on experience with at least one of the cloud AI solutions (AWS Bedrock/SageMaker, Azure AI).
Docker, Kubernetes, and Git for version control and deployment.
Deep experience with RESTful and GraphQL APIs to connect disparate systems.
Bachelor's or Masters degree in Cybersecurity, Computer Science, Artificial Intelligence or related field, or relevant industry certifications. Equivalent work experience is equally preferable.

Nice To Haves

GIAC Machine Learning Engineer
AWS Certified Machine Learning Engineer
Microsoft Certified: Azure AI Engineer Associate
Certified Information Systems Security Professional (CISSP)

Responsibilities

Build and maintain AI agents capable of multi-step reasoning and tool-use to automate cyber security use cases such as Autonomous Incident Investigation, vulnerability prioritization.
Design high-precision, version-controlled prompts with ability to programmatically optimize LLM outputs for reliable security logic and automated remediation scripts.
Use and fine-tune machine learning models (like Deep Learning, K-MEANS, SVM etc) to identify sophisticated attack patterns that bypass traditional signature-based defenses.
Integrate large language models (OpenAI, Anthropic, or open-source models into production environments via APIs or local deployments.
Develop and optimize data ingestion pipelines, specifically focusing on Vector Databases.
Audit existing manual workflows and re-engineer them using a "Machine-First" mindset, utilizing LLMs to handle unstructured data.
Deploy and monitor AI solutions using cloud-native tools, ensuring high availability, low latency, and cost-efficiency.
Implement guardrails for AI outputs to ensure ethical standards, data privacy, and "Human-in-the-loop" checkpoints where necessary.