Designation: Principal AI Engineer
Location: Concord, CA (Onsite)
Job Type: Contract
Duration: Long term
Key Responsibilities
Own and define enterprise‑grade GenAI architectures, including RAG pipelines, agentic workflows, prompt orchestration, and multi‑model routing strategies.
Drive production readiness for GenAI solutions, ensuring scalability, resilience, observability, and cost efficiency.
Lead complex architectural decisions spanning data ingestion, vector databases, model selection, guardrails, latency optimization, and API scalability.
Establish reference architectures and reusable patterns to accelerate GenAI adoption across Lines of Business.
Enterprise Use‑Case Delivery (FDE Model)
Act as a Forward Deployed Principal Engineer, partnering directly with business and product teams to deliver high‑impact GenAI use cases from ideation to production.
Translate business problems into well‑defined AI system designs and NFRs, ensuring measurable outcomes.
Troubleshoot and resolve complex production issues across non‑prod and prod environments.
Influence platform roadmap by feeding real‑world use‑case requirements back into central AI services (e.g., Tachyon).
Governance, Risk & Compliance
Ensure all AI solutions align with Wells Fargo risk, cyber, and model governance standards, including AIRR, data protection, and guardrails.
Partner with Cyber, MRM, Legal, and Risk teams to design compliant AI patterns without slowing delivery.
Embed security, privacy, and ethical AI principles into solution design by default.
Organizational Influence & Mentorship
Serve as a recognized GenAI expert across the enterprise—regularly consulted by engineering, architecture, and leadership teams.
Mentor senior and staff‑level engineers; raise the technical bar across teams.
Contribute to internal communities of practice, architecture reviews, and executive‑level technical discussions.
Represent Wells Fargo GenAI capabilities in internal innovation forums and knowledge‑sharing sessions
Required Qualifications
7+ years of software engineering experience, with significant depth in AI/ML or data‑intensive systems.
2+ Years of experience in Python programming language.
2+ Proven experience designing and delivering production‑scale Generative AI systems in an enterprise environment.
2+ years of experience in:
LLMs, prompt engineering, and RAG architecture
Vector databases and semantic search
API‑driven, cloud‑native architectures
Distributed systems and performance optimization
Preferred Qualifications
Strong understanding of non‑functional requirements: security, scalability, resiliency, observability, and cost management.
Demonstrated Principal‑level impact: influence across multiple teams, platforms, or business units.
Experience operating in highly regulated environments (financial services strongly preferred).
Hands‑on experience with agentic AI frameworks and multi‑model orchestration.
Prior experience in a Forward Deployed Engineer or embedded engineering model.
Ability to communicate complex technical concepts clearly to senior executives and non‑technical stakeholders.
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