Software Engineering Senior Manager

Wells Fargo Bank•Charlotte, NC

2d•Hybrid

About The Position

Wells Fargo is 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 senior technical leadership role combines engineering and people leadership, architectural depth, software-engineering expertise, production ownership, and digital threat protection. The leader is expected to remain technically engaged, contribute to architecture and implementation discussions, and guide platform and AI strategy while making consequential decisions about customer experience, protection effectiveness, and production reliability. The role will evolve DEP beyond traditional bot detection to protect interactions among humans, applications, automated systems, and autonomous agents. It will lead business-critical platforms, services, APIs, detection capabilities, and integrations; advance scalable and AI-enabled detection; modernize legacy and vendor-dependent capabilities; and guide production AI architecture and evaluation. The manager will build strong engineering teams and practices, own platform health, and shape DEP build, buy, and partner strategy. The position offers a hybrid work schedule and is available at the listed Wells Fargo locations in Charlotte, North Carolina; Concord, California; and San Leandro, California. Relocation assistance and visa transfer or sponsorship are not available. The stated base pay range is $159,000 to $305,000, with possible incentive opportunities; pay may vary based on factors including skills, experience, performance, and work location.

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, with the 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 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 partners, 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 engineering 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 addressing scalability, resiliency, security, latency, observability, and maintainability.
Maintain technical depth to review implementations, APIs, detection logic, and AI-enabled solutions.
Guide modular, API-first architectures and 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; measure control effectiveness and adapt defenses as threats evolve.
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.
Lead development of AI- and LLM-based solutions that interpret activity, behavioral signals, interaction sequences, and telemetry to identify suspicious or emerging behavior.
Determine how 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 AI capability accuracy, explainability, operational performance, and protection value, and set criteria for moving experiments into production.
Prepare DEP to evaluate interactions involving humans, traditional automation, software agents, and autonomous AI agents based on 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; develop technical leaders able to own complex platforms and architectural decisions.
Establish expectations for code quality, automated testing, deployment safety, observability, vulnerability remediation, documentation, and production ownership.
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 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; ensure incidents and control degradation lead to durable improvements.
Develop a balanced build, buy, and partner strategy for DEP, evaluating effectiveness, 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 reliance on proprietary technologies while maintaining strategic vendor partnerships.
Influence investment priorities by communicating technical risk, customer impact, threat evolution, and engineering opportunities.
Fulfill applicable risk and compliance responsibilities, including monitoring, governance, issue escalation and remediation, and sound risk decisions.