AI Engineer

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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.

Kaleidoscope, an Infosys Company, is an equal opportunity employer, and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, spouse of protected veteran, or disability.