Principal ML Engineer

Wells Fargo & Company•Iselin, NJ

About The Position

COO Technology operates at the center of the enterprise, enabling the Chief Operating Office to execute with precision, resilience, and confidence. Supporting everything from operational execution and regulatory assurance to customer experience, shared services, and supply chain, this organization is redefining how complex operations are understood, optimized, and continuously improved through data and technology. We are seeking a Senior Lead AI Engineer to help unlock the intelligence hidden within enterprise process data. In this role, you will sit at the intersection of Analysis & Evaluation and Process Improvement, applying advanced ML techniques to transform raw, extracted operational data into clear insights about how work actually gets done—and how it can be done better. Your work will power pattern discovery, process similarity analysis, and execution conformity at enterprise scale, directly influencing AI‑driven transformation initiatives across the firm. You will design scalable agentic solutions, ML systems that reconcile fragmented entities, surface meaningful clusters of behavior, and extract canonical execution patterns from clickstream data, documents, and knowledge graphs. By enriching enterprise knowledge graphs with continuously learned insights and measuring real‑world execution against documented procedures, you will enable a new level of process transparency, accountability, and automation. This is a high‑impact opportunity for someone who wants to shape how machine learning drives measurable operational improvement across a complex, regulated environment.

Requirements

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.
3+ years of experience with clustering algorithms and unsupervised learning techniques for pattern discovery.
2+ years of experience working with knowledge graphs or graph-based ML techniques.
5+ years of experience with Python with experience in ML frameworks.

Nice To Haves

Experience with agentic data retrieval and analysis at the Enterprise Level.
3+ years of experience building entity resolution, deduplication, or record linkage systems at scale.
Expertise in NLP techniques for semantic similarity, text clustering, and information extraction.
Experience with graph databases.
Background in process mining, conformance checking, or business process analysis.
Experience with LLMs and embedding models for semantic similarity.
Experience building ML pipelines using MLOps best practices.
Experience with cloud computing platforms.
Experience with distributed computing frameworks.
Knowledge of containerization and orchestration technologies.
Experience in financial services or operations domains.
Excellent communication skills across technical and non-technical audiences.
Advanced degree (M.S. or Ph.D.) in Computer Science, Machine Learning, or related field.

Responsibilities

Entity Deduplication & Resolution: Design and implement scalable ML pipelines for entity deduplication across knowledge graphs, handling entities extracted from diverse sources (transcripts, documents, structured data).
Intelligent Clustering: Build clustering algorithms to identify similar patterns across processes, participants, and execution paths.
Pattern Extraction: Develop ML models to extract canonical patterns of process execution from clickstream data and. documentation.
Knowledge Graph Enrichment: Create ML-powered pipelines that continuously enrich knowledge graphs with discovered patterns, relationships, and insights.
Conformity Analysis: Build models to calculate and analyze conformity scores comparing actual process execution against documented procedures.
Scalable Architecture: Design ML solutions that operate at enterprise scale, handling large volumes of process data and documentation.
Cross-Workstream Collaboration: Work with Analysis & Evaluation team on pattern discovery from clickstream data and with Process Improvement team on process similarity analysis.