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Job Specs - AI Engineering Lead

About this role
Wells fFargo is seeking a AI engineering Lead in CIB

In This role you will
• Lead end-to-end design and development of AI systems including chatbots, Retrieval Augmented Generation (RAG) platforms, autonomous agents, and workflow automation tools.
• Drive complex, multi-domain AI initiatives, ensuring technical excellence across backend services, front-end experiences, and AI model integrations.
• Architect scalable and secure, AI solutions leveraging Google, OpenAI, Anthropic, Vertex AI, and GitHub's AI ecosystem.
• Oversee enterprise-grade deployment pipelines using OpenShift (OCP), Kubernetes, and robust CI/CD patterns to ensure reliable production delivery.
• Establish engineering best practices across Python, TypeScript, and React for modern, AI-enabled applications.
• Guide prompt engineering, skill engineering, and model evaluation approaches including guardrails, observability, red teaming, and hallucination detection.
• Lead and mentor engineering pods , providing technical direction, career development, and architectural guidance.
• Ensure systems are fully observable, monitored, and resilient, integrating fallback logic and safety controls to mitigate model and system failures.
• Partner closely with UI/UX, platform, cybersecurity, and governance teams to deliver compliant, user-centric, and secure AI solutions at enterprise scale.
• Collaborate with business stakeholders and product owners to align AI capabilities with strategic outcomes and define delivery roadmaps.
• Ensure compliance with cybersecurity, data privacy, and model risk management standards required in regulated financial-services environments.
• Resolve complex technical issues, escalating and troubleshooting challenges across AI pipelines, backend services, front-end apps, and infrastructure.
• Lead Agile delivery processes, driving sprint execution, backlog prioritization, risk mitigation, and cross-team coordination.
• Be well versed in emerging AI technologies, staying current with LLM advancements, evaluation techniques, vector search, and responsible AI practices.
• Collaborate and influence across all levels, including senior managers, platform teams, and cross-functional partners to advance the AI engineering strategy.

Required Qualifications:

• 10+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• Expert-level proficiency in:
  • Python (AI/ML, backend, orchestration)
  • TypeScript and modern React frameworks
• Experience building solutions using:
  • OpenAI, Anthropic, Google (Vertex AI), GitHub Copilot / GitHub ecosystem
• Hands-on experience deploying applications using:
  • OCP/OpenShift, , Kubernetes, CI/CD pipelines
• Proven ability to lead multiple engineering pods and mentor engineers across levels.
• Comfortable executing Agile methodologies across short sprint cycles.
• Strong understanding of:
  • Cybersecurity controls
  • Model risk management
  • Compliance requirements in regulated financial-services environments

Desired Qualifications:

• Experience building high-availability banking or financial applications.
• Familiarity with vector databases, embeddings, search systems (e.g., ChromaDB,, Elasticsearch, Redis Vector).
• Understanding of evaluation frameworks for LLM outputs (hallucination detection, guardrails, red-teaming).
• Exposure to MLOps fundamentals and responsible AI concepts.

Job Expectations:

Key Responsibilities

Technical Leadership

• Lead end-to-end design and implementation of AI systems including chatbots, Retrieval-Augmented Generation (RAG), autonomous agents, and workflow automation tools.
• Architect scalable, secure solutions leveraging industry-leading AI providers (OpenAI, Anthropic, Google Vertex AI, GitHub Copilot).
• Oversee production deployment pipelines using OCP (OpenShift Container Platform) for containerization, orchestration, and runtime operations.
• Define best practices for Python, TypeScript, and React development within AI-enabled applications.

Project Delivery & Team Management

• Manage and mentor pod teams (approx. 4 / 6 engineers each), ensuring high-quality execution and technical rigor.
• Drive delivery roadmaps, project sequencing, and risk mitigation across multiple concurrent AI initiatives.
• Partner with UI/UX designers to build intuitive, compliant, and enterprise-ready user experiences.
• Guide prompt engineering, skill engineering, and evaluation frameworks for AI model tuning and safety.

Operational Excellence

• Ensure all AI deployments meet enterprise security, data privacy, and regulatory compliance standards required in Corporate & Investment Banking.
• Collaborate with cybersecurity, risk, and governance teams to enforce secure coding, model-handling, and data-access patterns.
• Design robust observability, monitoring, and fallback strategies for AI-driven production systems.

Collaboration & Stakeholder Engagement

• Work closely with product owners, business stakeholders, and platform teams to align AI capabilities with business outcomes.
• Communicate feasibility, architectural trade-offs, delivery timelines, and technical risks.
• Stay current with industry trends, emerging AI technologies, model evaluation techniques, and AI governance standards.