Senior Specialty AI Engineer

• Build and maintain ingestion pipelines: document parsing, chunking, embeddings, and metadata tagging.
• Implement retrieval strategies such as dense search, hybrid retrieval (BM25 + vector), and reranking.
• Configure and manage vector databases (e.g., Pinecone, Weaviate, FAISS).

Vertex AI & Cloud Engineering

• Develop and deploy services using Google Vertex AI (model endpoints, pipelines, vector search).
• Assist in containerization (Docker) and deployment via Kubernetes/GKE.
• Contribute to CI/CD workflows (GitHub Actions, Cloud Build)., * Work closely with product, data, and platform teams to deliver features.
• Contribute to engineering best practices (code quality, testing, documentation).
• Learn and adopt emerging GenAI tools, frameworks, and patterns., Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit's risk appetite and all risk and compliance program 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
• 4 years of AI/ML Software Engineering experience, or equivalent
• Hands-on experience with LangChain (required) and exposure to LangGraph or similar orchestration frameworks.
• Experience building RAG pipelines (chunking, embeddings, retrieval, evaluation basics).
• Familiarity with vector databases (Pinecone, Weaviate, FAISS, or similar).
• Backend development experience in Python (FastAPI) or Node.js.
• Frontend experience with React or Next.js.
• Experience with Docker, basic Kubernetes concepts, and CI/CD pipelines.
• Understanding of GenAI evaluation concepts, observability basics, and prompt design.
• Knowledge of security fundamentals (API security, PII handling, secrets management).
• Strong problem-solving and communication skills., * Exposure to LangGraph advanced patterns (state machines, multi-agent flows).
• Experience with LlamaIndex or structured RAG (SQL/Graph RAG).
• Familiarity with rerankers (Cohere, bge) and retrieval optimization techniques.
• Experience integrating LLMs with enterprise tools, databases, or APIs.
• Basic knowledge of knowledge graphs or ontology design.
• Exposure to LLM observability tools (LangSmith, OpenTelemetry).

b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.