Software Engineering Senior Manager

Wells Fargo•Concord, CA

4d•$159,000 - $305,000•Hybrid

About The Position

The Software Engineering Senior Manager leads 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 that combines engineering management, architectural depth, software-engineering expertise, production ownership, and digital threat protection. The leader is expected to engage across organizational leadership, architecture and design reviews, implementation discussions, production reliability, threat analysis, and technology strategy. The role will evolve DEP beyond traditional bot detection to protect interactions among humans, applications, automated systems, and autonomous agents. It will advance scalable platforms and AI-enabled detection capabilities, guide platform and AI strategy, and make consequential decisions involving customer experience, protection effectiveness, and production reliability. The position is for an engineering leader who manages engineering teams while remaining technically engaged, including guiding AI-native detection engineering rather than simply managing AI adoption. The role includes leading technical strategy and architecture; advancing bot, automation, and abuse defenses; developing production AI- and LLM-based detection and agent strategies; building and developing engineering teams; owning production and platform health; and shaping build, buy, and partner strategy for DEP. It also involves cross-functional partnership, measurable technology and risk outcomes, and clear communication of technical risk, customer impact, threat evolution, and engineering opportunities. The position offers a hybrid work schedule and is based at one of the listed Wells Fargo locations in Charlotte, North Carolina; Concord, California; or San Leandro, California. Relocation assistance and visa transfer or sponsorship are not available. The listed base pay range is $159,000 to $305,000, with possible incentive opportunities. Eligible employees have access to a comprehensive benefits package.

Requirements

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

Nice To Haves

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 solutions 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.

Responsibilities

Lead teams responsible for business-critical DEP platforms, services, APIs, detection capabilities, and integrations.
Establish technical strategy aligned with business needs, customer experience, and evolving threats.
Lead architecture and design reviews covering scalability, resiliency, security, latency, observability, and maintainability.
Remain technically engaged and review implementations, APIs, detection logic, and AI-enabled solutions.
Guide modular, API-first architectures that support integration of signals, models, rules, vendors, and internal capabilities.
Modernize legacy and vendor-dependent capabilities while maintaining production stability and protection effectiveness.
Lead 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 with fraud, cybersecurity, authentication, digital product, architecture, infrastructure, and application engineering teams.
Balance protection effectiveness with customer experience, latency, operational complexity, and business risk; measure control effectiveness and adapt defenses as adversaries evolve.
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 deterministic controls.
Establish methods to measure accuracy, explainability, operational performance, and protection value of AI-based capabilities; set criteria for moving experiments into production.
Prepare DEP for interactions involving humans, traditional automation, software agents, and autonomous AI agents, including approaches that assess identity, intent, provenance, authorization, behavior, trust, and risk.
Apply AI to detection engineering, investigation, threat analysis, testing, software delivery, and operational support.
Recruit, develop, and retain engineers, technical leads, architects, and engineering managers.
Build a culture of technically engaged leadership and ensure teams understand the platforms and threats they address.
Set expectations for code quality, automated testing, deployment safety, observability, vulnerability remediation, documentation, and production ownership.
Develop technical leaders who can independently own 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.
Provide accountable leadership for 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 lead to durable improvements.
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 and reduce unnecessary dependency on proprietary technologies while maintaining strategic vendor partnerships.
Influence investment priorities by communicating technical risk, customer impact, threat evolution, and engineering opportunities.
Execute applicable risk programs and follow Wells Fargo policies and procedures, including timely escalation and remediation of issues and sound risk decisions.