Software Engineering Senior Manager

About this role:
We are seeking a Software Engineering Senior Manager to lead engineering teams responsible for Digital Experience Protection (DEP) capabilities that protect customer interactions from sophisticated bots, account abuse, fraudulent automation, and emerging agent-driven threats. This is a senior technical leadership role combining engineering leadership, architectural depth, software-engineering expertise, production ownership, and digital threat protection. The successful candidate will move comfortably between organizational leadership, architecture and design reviews, implementation discussions, production reliability, threat analysis, and technology strategy. This leader will help evolve DEP beyond traditional bot detection toward protecting interactions among humans, applications, automated systems, and autonomous agents, while advancing scalable platforms and AI-enabled detection capabilities.
This position is intended for an engineering leader who manages, rather than a general manager who happens to lead engineering teams. The successful leader will combine strong people leadership and technical judgment engaging constructively with architects and senior engineers, bringing alternative perspectives and respectfully challenging assumptions when appropriate. They will guide platform and AI strategy, and make consequential decisions involving customer experience, protection effectiveness, and production reliability. They will have the prowess to guide the engineering of AI-native detection capabilities, not simply manage the adoption of AI technologies.
In this role, you will:

• Provide Technical and Architectural Leadership
  • Lead engineering teams responsible for business-critical DEP platforms, services, APIs, detection capabilities, and integrations.
  • Establish technical strategy aligned with business needs, customer experience, and the evolving threat environment.
  • Lead architecture and design reviews covering scalability, resiliency, security, latency, observability, and maintainability.
  • Maintain sufficient coding and technical depth to review implementations, APIs, detection logic, and AI-enabled solutions.
  • Guide development of modular, API-first architectures that enable rapid integration of new signals, models, rules, vendors, and internal capabilities.
  • Lead modernization of legacy and vendor-dependent capabilities while maintaining production stability and protection effectiveness.
• Advance Bot, Automation, and Abuse Defense
  • Lead engineering strategy for detecting bots, scripted automation, credential-based attacks, account abuse, automated fraud, and emerging machine-driven threats.
  • Guide layered detection and decisioning using network, device, behavioral, identity, account, transaction, and historical risk signals.
  • Translate attack patterns, control gaps, and adversary behavior into scalable technical capabilities.
  • Partner across fraud, cybersecurity, authentication, digital product, architecture, infrastructure, and application engineering.
  • Balance protection effectiveness with customer experience, latency, operational complexity, and business risk.
  • Measure control effectiveness and adapt defenses as adversaries evolve.
• Lead AI-Native Detection and Agent Strategy
  • Lead development of AI- and LLM-based solutions that interpret user activity, behavioral signals, interaction sequences, and telemetry to identify suspicious or emerging behaviors.
  • Determine where LLM reasoning, machine learning, deterministic rules, and behavioral analytics should be used independently or together.
  • Guide production AI architecture, including model integration, grounding, tool calling, evaluation, observability, security, reliability, latency, and appropriate deterministic controls.
  • Establish methods for measuring the accuracy, explainability, operational performance, and protection value of AI-based capabilities.
  • Drive experimentation while establishing clear criteria for moving AI solutions into production.
  • Prepare DEP for interactions involving humans, traditional automation, software agents, and autonomous AI agents.
  • Develop approaches for evaluating automated interactions based on identity, intent, provenance, authorization, behavior, trust, and risk.
  • Apply AI to improve detection engineering, investigation, threat analysis, testing, software delivery, and operational support.
• Build and Lead Strong Engineering Teams
  • Recruit, develop, and retain engineers, technical leads, architects, and engineering managers.
  • Build a culture in which leaders remain technically engaged and teams understand both the platforms and threats they address.
  • Establish expectations for code quality, automated testing, deployment safety, observability, vulnerability remediation, documentation, and production ownership.
  • Develop technical leaders capable of independently owning complex platforms and architectural decisions.
  • Reduce knowledge concentration through secondary ownership, cross-training, reviews, rotations, documentation, and succession planning.
  • Balance delivery with modernization, reliability, technical-debt reduction, and engineering capability development.
  • Hold teams accountable for measurable technology and risk outcomes.
• Own Production and Platform Health
  • Provide accountable leadership for the performance, availability, resiliency, and engineering health of DEP capabilities.
  • Drive monitoring, capacity planning, resilience testing, incident prevention, root-cause analysis, and operational automation.
  • Establish failure-handling and graceful-degradation strategies for internal and external dependencies.
  • Lead trade-off decisions involving customer experience, protection effectiveness, and platform stability.
  • Ensure incidents and control degradation result in durable technical and operational improvements.
• Shape Platform and Vendor Strategy
  • Develop a balanced build, buy, and partner strategy for DEP.
  • Evaluate solutions based on effectiveness, data and signals, explainability, integration flexibility, engineering effort, operational ownership, cost, and time to capability.
  • Identify where internal AI and detection capabilities can provide differentiated value.
  • Reduce unnecessary dependency on proprietary technologies while maintaining strategic vendor partnerships.
  • Influence investment priorities by clearly communicating technical risk, customer impact, threat evolution, and engineering opportunities.

Required Qualifications:

• 7+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 3+ years of management or leadership experience
• 5+ years of software-engineering background with the ability to participate meaningfully in code, API, architecture, data, AI, and system-design discussions.
• 3+ years of experience designing, modernizing, or operating highly available and high volume distributed systems.
• 3+ years leading architectural decisions involving scalability, resiliency, latency, security, and observability.
• 3+ developing engineers and technical leaders and establish strong engineering practices as a manager or lead technologist
• 3+ years managing production systems, including production incident management, root-cause analysis, resilience, and continuous improvement.
• 3+ years evaluating technical trade-offs across customer experience, risk, platform stability, cost, and delivery.
• 3+ years of experience communicating complex technical topics effectively to engineers, senior leaders, business and risk partners; expert stakeholder management

Desired Qualifications:

• Experience with bot management, application security, digital identity, authentication, fraud controls, or abuse prevention.
• Knowledge of behavioral signals, device and network intelligence, client telemetry, risk scoring, and decision orchestration.
• Experience with commercial bot-management technologies such as Arkose Labs, Akamai Bot Manager, or comparable platforms.
• Experience with behavioral biometrics, anomaly detection, risk engines, or fraud-detection platforms.
• Experience designing or operating production AI/LLM solutions, particularly those interpreting behavioral, event, transaction, or telemetry data.
• Experience combining LLM reasoning with deterministic rules, machine learning, or behavioral analytics.
• Working knowledge of LLMs, retrieval-augmented generation, model gateways, tool calling, AI agents, evaluation, and AI-assisted engineering.
• Understanding of adversarial AI and how generative AI and autonomous agents can change automated threats.
• Experience modernizing high-volume platforms or building internal capabilities that reduce reliance on proprietary technologies.

Job Expectations:

• This position offers a hybrid work schedule
• Relocation assistance is not available for this position
• Visa transfer and sponsorship is not available for this position

Locations:

• 1525 W. W.T. Harris Blvd., Charlotte, North Carolina
• 1755 Grant St., Concord, California
• 794 Davis St., San Leandro, California

Pay Range
Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to demonstrated examples of prior performance, skills, experience, or work location. Employees may also be eligible for incentive opportunities.
$159,000.00 - $305,000.00
Benefits
Wells Fargo provides eligible employees with a comprehensive set of benefits, many of which are listed below. Visit Benefits - Wells Fargo Jobs for an overview of the following benefit plans and programs offered to employees.

• Health benefits
• 401(k) Plan
• Paid time off
• Disability benefits
• Life insurance, critical illness insurance, and accident insurance
• Parental leave
• Critical caregiving leave
• Discounts and savings
• Commuter benefits
• Tuition reimbursement
• Scholarships for dependent children
• Adoption reimbursement

Posting End Date:
14 Nov 2026
* Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit's risk appetite and all risk and compliance program requirements.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo .
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
Wells Fargo Recruitment and Hiring Requirements:
a. Third-Party recordings are prohibited unless authorized by Wells Fargo.
b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.
c. Please be prepared to provide accurate information and supporting documentation in connection with any lawful employment and education verification conducted by Wells Fargo.
#BI-Hybrid