Lead Applied AI Engineer

Wells Fargo•Iselin, NJ

6d•$159,000 - $305,000

About The Position

The Cognitive AI Solutions Team is building the next generation of agentic AI and process intelligence capabilities for the firm. As our platform matures, partner organizations across the bank; banking operations, complaints, wholesale operations, and more are engaging us to bring advanced AI directly to their highest-impact business problems. We are seeking a Lead Applied AI Engineer to be the technical engine of that delivery: someone who takes the reusable components and platform capabilities we build internally and turns them into shipped, production agentic solutions inside the bank’s most important operational domains. This is a hands-on builder’s role with significant scope; and it is emphatically not a proof-of-concept job. You will own solutions end to end: from sitting with business teams to understand how their work really flows, to designing the agentic architecture, to shipping systems that meet the bank’s reliability, security, and compliance standards and deliver measurable outcomes. The right candidate is creative, moves fast, communicates as fluently with operations leaders as with platform engineers, and has a track record of turning ambiguous business problems into working software in weeks.

Requirements

5+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
5+ years of software engineering experience, with at least 3 years applying machine learning or AI to production systems.

Nice To Haves

Hands-on experience building and deploying machine learning models, including transformer-based architectures or foundation models, in a production environment.
Strong full-stack software engineering skills—comfortable across data, models, services, and integration; able to ship end-to-end.
Solid understanding of modern AI evaluation, observability, and guardrails for production deployment.
Excellent collaboration, technical communication, and problem-solving skills, with a track record of shipping software that touches real users in real production environments.
Prior experience in financial services applications (e.g., banking operations, complaints, wholesale operations) or comparable regulated enterprise domains.
Experience working under Model Risk Management or compliance governance for production AI deployment.
Experience working with large-scale transactional or sequential data.
Experience working in a partner-facing engineering engagement model with business organizations.

Responsibilities

Deliver agentic AI solutions in production. Design, build, and ship AI agents for a partner business domain using the team’s internal platform and reusable components — owning the solution from first workshop to production operation.
Partner deeply with the business. Embed with business organizations to understand their workflows, data, and operational touchpoints; identify where agentic AI delivers measurable improvement; and translate domain knowledge into technical designs.
Build quickly and iterate. Compose solutions from platform capabilities rather than rebuilding from scratch; prototype fast, validate with users, then harden for production. Speed with rigor is the job.
Apply state-of-the-art techniques. Use foundation models, LLM orchestration, retrieval, and transformer-based approaches for sequential and transactional data — choosing the simplest technique that ships.
Own production quality. Build the evaluation, observability, and guardrail systems for everything you ship; ensure deployed agents meet the bank’s reliability, security, and compliance standards.
Strengthen the platform. Contribute improvements, patterns, and reusable components back to the platform team so every delivery makes the next one faster.
Communicate outcomes. Translate technical progress into presentable results — supporting partner stakeholder reviews and executive demos with clarity and credibility.