Principal Engineer

Wells Fargo Bank•Charlotte, NC

13d•Hybrid

About The Position

Wells Fargo is seeking a senior engineering leader to support Fargo , the enterprise Virtual Assistant and GenAI platform, which powers conversational, AI‑driven experiences across multiple lines of business. This role will focus on large‑scale platform engineering, AI enablement, and cross‑enterprise integration , operating at the intersection of architecture, security, and business transformation. In this role, you will: Act as a technical and architectural advisor to senior leadership for an enterprise GenAI and conversational platform , influencing design and implementation of highly complex applications, APIs, AI/ML integrations, streaming technologies, security controls, and cloud‑native platforms across multiple enterprise teams Lead strategy and resolution of complex, cross‑domain challenges involving GenAI, conversational platforms, API enablement, authentication/authorization, data domains (EDE), and enterprise compliance, delivering scalable, long‑term solutions Translate advanced engineering experience and a deep understanding of enterprise AI strategy, platform roadmaps, and business objectives into pragmatic, production‑ready technical solutions Provide vision and direction on GenAI adoption, including LLM integration patterns, prompt orchestration, model governance, observability, and responsible AI controls within an enterprise AI platform Drive architectural alignment for real-time and near-real-time experiences, including streaming (e.g., HTTP 1.1 SSE), event‑driven integrations, and performance optimization for conversational entry points Partner closely with product, security, platform, and data teams to ensure enterprise AI solutions meet security, privacy, resiliency, and regulatory standards Maintain awareness of industry best practices in AI platforms, conversational systems, and cloud‑native engineering, recommending innovations that improve customer experience, developer productivity, and operational efficiency Strategically engage with engineering leaders, architects, and executives across the enterprise, serving as a trusted expert and thought leader for enterprise GenAI initiatives

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
3+ years of experience in AI/ML technologies, including Large Language Models (LLMs), prompt engineering, model integration, or applied machine learning in production environments
3+ years of experience with distributed streaming and event‑driven architectures, using technologies such as Kafka, Flink, or similar real‑time data processing frameworks
5+ years of experience in database design and data architecture, including relational and/or NoSQL systems, data modeling, and high‑throughput data access patterns
7+ years of programming experience in Java and/or Python, building scalable, production‑grade backend systems and APIs
3+ years of experience designing and integrating APIs and microservices, including REST/gRPC services and service orchestration patterns
3+ years experience working with cloud‑native platforms and architectures, including containerization, distributed systems, and scalable deployment models
5+ years experience with system design and architecture for high‑scale applications, including performance optimization, fault tolerance, and resiliency
3+ years working and understanding of security, data privacy, and governance considerations, especially in regulated enterprise environments

Nice To Haves

Experience with conversational platforms, chatbots, or virtual assistants at enterprise scale
Hands‑on experience with GenAI / LLM integrations , including prompt management, orchestration layers, and AI safety or governance controls
Knowledge of API security, OAuth, authentication/authorization platforms , and reverse‑proxy or gateway patterns
Experience working with streaming or real‑time technologies to support low‑latency user experiences
Familiarity with enterprise data domains (e.g., EDE) and data access patterns for AI‑driven applications
Ability to operate effectively in highly matrixed organizations , influencing without direct authority

Responsibilities

Act as a technical and architectural advisor to senior leadership for an enterprise GenAI and conversational platform , influencing design and implementation of highly complex applications, APIs, AI/ML integrations, streaming technologies, security controls, and cloud‑native platforms across multiple enterprise teams
Lead strategy and resolution of complex, cross‑domain challenges involving GenAI, conversational platforms, API enablement, authentication/authorization, data domains (EDE), and enterprise compliance, delivering scalable, long‑term solutions
Translate advanced engineering experience and a deep understanding of enterprise AI strategy, platform roadmaps, and business objectives into pragmatic, production‑ready technical solutions
Provide vision and direction on GenAI adoption, including LLM integration patterns, prompt orchestration, model governance, observability, and responsible AI controls within an enterprise AI platform
Drive architectural alignment for real-time and near-real-time experiences, including streaming (e.g., HTTP 1.1 SSE), event‑driven integrations, and performance optimization for conversational entry points
Partner closely with product, security, platform, and data teams to ensure enterprise AI solutions meet security, privacy, resiliency, and regulatory standards
Maintain awareness of industry best practices in AI platforms, conversational systems, and cloud‑native engineering, recommending innovations that improve customer experience, developer productivity, and operational efficiency
Strategically engage with engineering leaders, architects, and executives across the enterprise, serving as a trusted expert and thought leader for enterprise GenAI initiatives
Drive architectural consistency and technical excellence across Fargo integrations
Balance innovation with risk management, compliance, and operational stability
Communicate complex technical concepts clearly to executive and non‑technical stakeholders
Support cross‑LOB onboarding and adoption of Fargo capabilities