Senior Cyber Threat Defense AI/ML Engineer

Bank of America

. Build and integrate advanced AI and machine learning capabilities into Bank of America's cyber defense ecosystem .

Posted 10/21/2026full-timeUnited StatesSenior💰 $145,000 - $192,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and integrating AI and machine learning capabilities for cybersecurity, with a strong focus on model development, deployment, and operationalization. Proficient in leveraging cloud AI platforms and ensuring responsible AI practices within security frameworks.

Highest-signal resume keywords

Machine Learning EngineeringPython ProficiencyAI GovernanceCybersecurity FundamentalsMLOps Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningFeature EngineeringModel EvaluationLarge Language ModelsDeterministic AutomationGenerative AIData PipelinesModel DeploymentExperiment TrackingAdversarial Machine Learning

Soft Skills

Strong CommunicationPresentation SkillsMentoring

Tools & Technologies

PyTorchScikit-LearnAzure AI FoundryAmazon BedrockGoogle Cloud Vertex AIAI-Enhanced SOAR Platforms

Industry Keywords

Cyber DefenseThreat HuntingIncident ResponseSecurity TelemetryRegulated IndustriesModel Risk Management

Tech Stack

Tools & technologies

AzureCloudCyber SecurityPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

Build and integrate advanced AI and machine learning capabilities into Bank of America's cyber defense ecosystem
Engineer deterministic and scripted automation, custom machine learning models, large language models, and agentic AI systems
Design, build, and deploy AI-powered capabilities for threat hunting, anomaly detection, and automated incident response over large-scale security telemetry
Develop and operationalize custom machine learning models and LLM-based workflows for cybersecurity use cases
Own the lifecycle from data preparation and feature engineering through training, evaluation, deployment, and monitoring
Apply deterministic automation, classical machine learning, generative AI, or combinations based on problem structure, data, and operational risk
Build LLM-based tooling for investigation support, detection engineering assistance, and knowledge retrieval
Partner with GIS operational and technical teams to identify AI-driven enhancements to security controls and architecture
Prototype and evaluate emerging AI technologies for cyber threat detection and response
Collaborate with offensive security teams on AI-enhanced red teaming and adversarial emulation capabilities
Contribute to architectural decisions supporting scalable and governed AI integration across GIS security controls
Promote responsible and ethical AI use, including bias mitigation and explainability
Advise senior leadership and mentor engineers and analysts as a technical expert on AI-driven cybersecurity initiatives

Requirements

What you’ll need

7+ years of hands-on machine learning engineering experience, including fine-tuning, evaluating, and deploying custom models in production
Strong command of machine learning fundamentals, including model training and evaluation, feature engineering, embeddings, class imbalance, label noise, and model drift
Proficiency in Python and hands-on experience with ML frameworks such as PyTorch or scikit-learn
Experience with model evaluation harnesses and experiment tracking
Hands-on experience building LLM-powered applications and agentic AI systems
Experience delivering production systems at scale involving data pipelines, model deployment, MLOps, and automation
Experience with enterprise cloud AI development platforms such as Azure AI Foundry, Amazon Bedrock, or Google Cloud Vertex AI, or equivalent infrastructure
Working understanding of cybersecurity fundamentals and defensive operations
Familiarity with AI governance and model risk management, including model validation, explainability, and responsible AI
Strong communication and presentation skills
Bachelor's degree in computer science, a related quantitative field, or equivalent applied experience
Experience applying ML or AI to cybersecurity problems
Experience with security telemetry at scale, such as EDR, SIEM, network, or identity data
Understanding of offensive security tactics, threat actor behaviors, red teaming, attack path mapping, and threat modeling
Hands-on offensive security experience, such as CTF competitions, red team tooling development, or published security research
Experience with the open-source LLM ecosystem, LLM guardrails, and agent orchestration frameworks
Familiarity with adversarial machine learning and AI security risks
Experience with AI-enhanced SOAR platforms
Prior work in regulated industries, including model risk and compliance considerations in financial services

Benefits

Comp & perks

Discretionary incentive eligible; eligible to participate in the annual discretionary plan
Annual discretionary award based on individual performance, line of business/group performance, and overall Company success
Industry-leading benefits
Paid time off
Resources and support to employees
40 hours per week
1st shift