Job Description: At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day. Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve. Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! Job Description: 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. Role 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. Required Qualifications 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. Desired Qualifications 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. Skills: Artificial Intelligence Critical Thinking Threat Analysis Cyber Security Data Privacy and Protection Data and Trend Analysis Stakeholder Management This job will be open and accepting applications for a minimum of seven days from the date it was posted. Shift: 1st shift (United States of America) Hours Per Week: 40 Pay Transparency details US - CO - Denver - 1144 15th St - Denver Gis (CO9926), US - DC - Washington - 1800 K St NW - 1800 K Street NW (DC1842), US - IL - Chicago - 540 W Madison St - Bank Of America Plaza (IL4540), US - MA - Boston - 100 Federal St - 100 Federal St Lp (MA5100), US - NJ - Jersey City - 101 Hudson St - 101 Hudson (NJ2101) Pay and benefits information Pay range $145,000.00 - $192,500.00 annualized salary, offers to be determined based on experience, education and skill set. Discretionary incentive eligible This role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on their overall individual performance results and behaviors, the performance and contributions of their line of business and/or group; and the overall success of the Company. Benefits This role is currently benefits eligible. We provide industry-leading benefits, access to paid time off, resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve. Bank of America is committed to help employees through the transition period when they’re displaced as a result of a workforce reduction, realignment or similar measure. Please review the resume writing and interviewing tips provided below to help prepare you for your next career opportunity. Getting started Regardless of the position you are interested in, the starting points to building your resume are the same: 1. Determine the job or types of jobs you want to do and research their responsibilities and qualifications. 2. Think about why you can do the job and make a list of your skills that are relative to the job. 3. Identify experiences or accomplishments that show your proficiency in the skills required for the job. 4. Summarize your abilities, accomplishments and skills into a brief, concise document. Considerations when writing a resume • Do be brief. Resumes should be 1-2 pages in length. • Do be upbeat and active in your wording. • Do emphasize what you have done clearly and concretely. • Do be neat and well organized. • Do have others proofread and critique your resume. Spell check. Make it error free. • Do use high quality, white or light colored 8½ x 11 paper. Use a laser printer if possible. • Don't be dishonest, always tell the truth about yourself in the most flattering light. • Don't include salary history or requirements. • Don't include references. • Don't include accomplishments that do not support your professional goals. • Don't include anything that isn't relevant. (For example, don't mention your fondness for swimming unless you want to work on the water.) • Don't use italics, underlining, shadows or other fancy treatments. Seven steps to a successful interview 1. Anticipate –Put yourself in the interviewer's position. What do you believe the interviewer is most interested in? Why do you think you have been invited to interview? 2. Research –What are the primary functions of the line of business? What are the success factors for the job? Is there a job description available? 3. Assess –Think about your skills, abilities, knowledge, interests, traits, values and accomplishments. Match them to what you know about the job. Consider which ones you should highlight. 4. Prepare Answers –Think about what the interviewer may ask, determine what the best answer is and write it down. 5. Prepare Questions – Interviewing is a two-way street. By asking thoughtful questions, you communicate your interest and learn a lot about the job. Choose two or three questions to ask your interviewer. Avoid asking a lot of questions about vacation time or breaks. 6. Practice – It may seem awkward, but it is the best way to come across well in an interview. Practice your own "great responses" with others or in front of a mirror until you appear relaxed and at ease. 7. Follow-up – Send a brief follow-up letter to the interviewer. Keep in mind that the many job searchers will not send a follow-up letter. Sending one can become a competitive advantage. Pay Transparency - https://careers.bankofamerica.com/en-us/pay-transparency Privacy Statement - https://careers.bankofamerica.com/en-us/privacy-notice

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