Lead AI Engineer

Wells Fargo & Company•Iselin, NJ

19h

About The Position

This role focuses on shaping the future of enterprise operations using Generative AI COO Technology, which powers critical enterprise systems from strategic execution to customer experience. The team is responsible for large-scale modernization, building resilient, data-driven platforms for operational excellence. The Lead Specialty Software Engineer will define and deliver next-generation Generative AI capabilities in a hands-on engineering role, working with distributed systems, streaming data, and AI at enterprise scale. Responsibilities include architecting and building production-grade AI pipelines, embedding modern observability into LLM and VLM systems, and ensuring reliability, performance, and cost transparency. The role involves designing scalable ingestion and streaming orchestration for high-volume clickstream and case-lifecycle data, deploying containerized services via CI/CD and infrastructure-as-code, and implementing rigorous monitoring, tracing, and resiliency patterns for AI systems. Additionally, the engineer will process video and transcript data for downstream analysis and integrate with evaluation and analytics workstreams, aiming to build innovative, trusted, and explainable AI platforms for business operations.

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.
2+ years of experience with streaming platforms for data orchestration.
2+ years of experience with containerization and container orchestration platforms
2+ years of deploying and operating ML/AI models in production environments.
3+ years of experience programming with Python.

Nice To Haves

5+ years of experience in software engineering with focus on data pipelines and distributed systems.
Experience with LLMOps/MLOps frameworks for observability.
Experience with Vision Language Models (VLMs) or multi-modal AI systems.
Experience with enterprise container platforms.
Background in video processing or computer vision pipelines.
Experience with observability tools and platforms.
Experience building resilient systems with patterns like circuit breakers, bulkheads, and retry policies.
Experience with cloud computing platforms.
Knowledge of infrastructure-as-code tools.
Experience with CI/CD pipelines.
Understanding of data quality and validation frameworks.
Experience in financial services or enterprise environments.
Excellent communication skills across technical and non-technical audiences.

Responsibilities

Pipeline Development: Build scalable, robust ingestion pipelines for processing clickstream data, handling both participant-level (full-day employee activity) and case-level (end-to-end case lifecycle) data streams.
Streaming Orchestration: Design and implement streaming platform-based orchestration for pipeline coordination, ensuring reliable data flow and processing guarantees.
Container Platform Deployment: Deploy and manage containerized services on enterprise container platforms, implementing CI/CD pipelines and infrastructure-as-code practices.
AI Model Observability: Implement comprehensive observability for LLM and VLM pipelines, including: Performance monitoring and metrics collection. Distributed tracing for multi-model pipelines. Logging and alerting for model inference. Cost tracking and optimization.
Resiliency Engineering: Build fault-tolerant systems with retry mechanisms, circuit breakers, dead letter queues, and graceful degradation patterns.
Video/Transcript Processing: Work with VLMs to process clickstream video data and generate high-quality transcripts for downstream analysis.
Integration: Ensure seamless integration with downstream Analysis & Evaluation workstream.