Generative AI (GenAI) Engineering Manager

Wells Fargo & Company•Iselin, NJ

21h•Hybrid

About The Position

About this role: Wells Fargo is seeking a Generative AI (GenAI) Engineering Manager who will lead the design, delivery, and operationalization of next‑generation AI/ML and GenAI platforms, capabilities, and solutions across the enterprise. This role will drive strategic AI initiatives, guide engineering teams, and partner closely with Architecture, Product, and business stakeholders to accelerate the adoption of responsible, secure, and scalable GenAI. In this role, you will: Manage, coach, and develop a team of AI/ML engineers, prompt engineers, and full‑stack developers delivering enterprise‑grade GenAI solutions. Lead design and development of LLM‑based applications, RAG pipelines, intelligent agents, and AI‑driven workflow automation. Ensure alignment with Enterprise Architecture, Responsible AI policies, LLM security controls, and all non-functional requirements (latency, scalability, resiliency, monitoring). Partner with architects to integrate cloud, data, and AI platform capabilities including model hosting, GPU compute, vector databases, and feature stores. Identify and resolve AI‑specific technical barriers such as model performance bottlenecks, data quality problems, prompt degradation, and deployment issues. Act as an escalation point for Agile teams, removing impediments and enabling innovation, experimentation, and continuous learning. Conduct peer reviews and model reviews to ensure engineering quality and alignment with strategic AI technical direction. Interpret, enforce, and optimize security, stability, and scalability guardrails; proactively identify and mitigate model, technology, and enterprise risks. Collaborate with business leaders to validate AI/ML opportunities and translate them into actionable engineering roadmaps. Provide L2 engineering support for production issues including model drift, integration failures, and data pipeline problems.

Requirements

4+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
2+ years of Leadership experience
4+ years of experience in AI/ML engineering management or related software engineering leadership role.
4+ years of programming experience (Python, Java, or similar).
4+ years of hands‑on experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit‑Learn, LangChain, LlamaIndex).
4+ years designing cloud‑native applications in GCP, AWS, or Azure.
4+ years of experience building LLM‑powered applications including embeddings, vector databases, model orchestration, and RAG pipelines.
4+years of experience with distributed and event‑driven systems (Kafka, Redis, MongoDB, Spark).
4+ years of Experience delivering APIs, microservices, and modern data solutions (Big Query, Snowflake, Dataproc).
4+years in test automation, performance evaluation, and enterprise‑grade AI validation.
4+years of Experience with UI/UX mockups (e.g., Figma) for AI workflow visualization.

Nice To Haves

Experience with Capital Markets products, trade lifecycle, risk, and P&L (preferred but not required)
Experience developing AI/ML solutions for risk modeling, market intelligence, data summarization, and workflow automation
Advanced cloud and AI/ML certifications such as Google Cloud Architect or Google AI/ML Professional
Demonstrated leadership in AI transformation, including workshops, training, and capability‑building across technical organizations
Hands‑on experience driving adoption of AI‑assisted engineering tools such as Cursor to improve developer productivity
Bachelor’s degree in computer science, Computer Engineering, Data Science, Artificial Intelligence, or related technical field
Strong strategic leadership and ability to guide teams through complex GenAI engineering challenges

Responsibilities

Manage, coach, and develop a team of AI/ML engineers, prompt engineers, and full‑stack developers delivering enterprise‑grade GenAI solutions.
Lead design and development of LLM‑based applications, RAG pipelines, intelligent agents, and AI‑driven workflow automation.
Ensure alignment with Enterprise Architecture, Responsible AI policies, LLM security controls, and all non-functional requirements (latency, scalability, resiliency, monitoring).
Partner with architects to integrate cloud, data, and AI platform capabilities including model hosting, GPU compute, vector databases, and feature stores.
Identify and resolve AI‑specific technical barriers such as model performance bottlenecks, data quality problems, prompt degradation, and deployment issues.
Act as an escalation point for Agile teams, removing impediments and enabling innovation, experimentation, and continuous learning.
Conduct peer reviews and model reviews to ensure engineering quality and alignment with strategic AI technical direction.
Interpret, enforce, and optimize security, stability, and scalability guardrails; proactively identify and mitigate model, technology, and enterprise risks.
Collaborate with business leaders to validate AI/ML opportunities and translate them into actionable engineering roadmaps.
Provide L2 engineering support for production issues including model drift, integration failures, and data pipeline problems.