Cyber Threat Defense Sr AI/ML Engineer

Bank of America•Jersey City, NJ

7d•$145,000 - $192,500•Onsite

About The Position

Bank of America's Global Information Security (GIS) team is seeking a Cyber Threat Defense Sr AI/ML Engineer to build and integrate advanced AI and machine learning capabilities into our cyber defense ecosystem. This person will drive innovation across preventative, detective, and responsive security controls by engineering the full spectrum of intelligent automation: deterministic and scripted automation, custom machine learning models, large language models (LLMs), and agentic AI systems. This is a senior individual contributor role balancing hands-on engineering with technical leadership, working closely with leadership and engineering teams to drive AI integration into cyber defense. This engineer will focus on applying AI to defend against modern threats, including threat actors who are themselves leveraging AI, and will partner with security subject matter experts across GIS on defending the bank's own use of AI. The ideal candidate is an experienced AI/ML engineer with deep fundamentals in machine learning theory and practice, strong production engineering discipline, and a working understanding of cybersecurity, who knows that the right solution is sometimes a script, sometimes a model, and sometimes an agent.

Requirements

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 (model weights, loss functions, precision, recall, F1, calibration), feature engineering, embeddings, and real-world data issues such as class imbalance, label noise, and model drift. Candidates whose AI experience consists primarily of using generative AI tools, agents, or APIs will not meet this bar; candidates should expect to discuss models they have personally trained and evaluated.
Proficiency in Python and hands-on experience with ML frameworks such as PyTorch or scikit-learn, including model evaluation harnesses and experiment tracking.
Hands-on experience building LLM-powered applications and agentic AI systems (e.g., retrieval-augmented generation, fine-tuning, tool use, orchestration), grounded in the ML fundamentals above.
Experience delivering production systems at scale involving data pipelines, model deployment, MLOps, and automation.
Experience with enterprise cloud AI development platforms (e.g., Azure AI Foundry, Amazon Bedrock, Google Cloud Vertex AI) or equivalent open-source or self-hosted model infrastructure.
Working understanding of cybersecurity fundamentals (the attack lifecycle, common attacker techniques, and defensive controls) and of how AI can enhance defensive operations.
Familiarity with AI governance and model risk management concepts, such as model validation, explainability, and responsible AI.
Strong communication and presentation skills, including the ability to translate complex technical concepts for senior executives and cross-functional stakeholders.
Bachelor's degree in computer science, a related quantitative field, or equivalent applied experience; advanced degree (MS/PhD) preferred.

Nice To Haves

Experience applying ML or AI to cybersecurity problems such as detection engineering, threat hunting, malware analysis, or security automation.
Experience working with security telemetry at scale (e.g., EDR, SIEM, network, or identity data).
Understanding of offensive security tactics and threat actor behaviors, and of how AI can enhance red teaming, attack path mapping, and threat modeling.
Hands-on offensive security experience (e.g., CTF competitions, red team tooling development, or published security research).
Experience with the open-source LLM ecosystem (e.g., Hugging Face, LangChain), LLM guardrails, and agent orchestration frameworks.
Familiarity with adversarial machine learning and AI security risks.
Experience with AI-enhanced SOAR (Security Orchestration, Automation, and Response) platforms.
Prior work in regulated industries, including model risk and compliance considerations in financial services.

Responsibilities

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 tailored to cybersecurity use cases, owning the lifecycle from data preparation and feature engineering through training, evaluation, deployment, and monitoring.
Match the technique to the problem: apply deterministic automation, classical machine learning, or generative AI (or a combination) based on the problem structure, the available data, and the operational risk profile.
Build LLM-based tooling that multiplies analyst effectiveness, such as investigation support, detection engineering assistance, and knowledge retrieval.
Partner with GIS operational and technical teams to identify opportunities for AI-driven enhancements to security controls and architecture.
Prototype and evaluate emerging AI technologies for applicability in cyber threat detection and response.
Collaborate with offensive security teams to develop AI-enhanced red teaming and adversarial emulation capabilities.
Contribute to architectural decisions that support scalable, well-governed AI integration across GIS security controls.
Promote responsible and ethical use of AI in security operations, partnering with model governance stakeholders on bias mitigation and explainability.
Act as a technical expert on AI-driven cybersecurity initiatives, advising senior leadership and mentoring engineers and analysts.