Lead Machine Learning Engineer

Wells Fargo•Charlotte, NC

4d•$119,000 - $224,000•Hybrid

About The Position

The AI/ML Data Architecture, Engineering, and Enablement team is seeking a Lead Machine Learning Engineer (Predictive AI) to design and deliver advanced solutions that support the full lifecycle of machine learning, from experimentation through production monitoring. The Data, Analytics, and Reporting Technology team supports Wells Fargo’s Global Operations, and drives a critical set of enterprise capabilities that power data-informed decision-making at scale. In this role, you will leverage Google Cloud Platform (GCP) services and modern ML frameworks to architect and operationalize scalable, reusable data and model pipelines. You will champion standardized frameworks, enable self-service for data scientists and domain experts, and embed governance-by-design to ensure secure, reliable, and compliant AI solutions that drive predictive insights, operational efficiency, and enterprise impact.

Requirements

5+ years of Database Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
5+ years of experience creating analytics or data science solutions in Public Cloud (GCP, AWS, Azure)
5+ years of hands on experience of Python and/or Go for building data pipelines, libraries, and automation tooling
5+ years with GCP or equivalent open source orchestration tools (Composer/Airflow/Dataflow/Beam) and CI/CD (Git, Liquibase, ) for data workloads
2+ years of hands-on experience building and implementing predictive AI models using machine learning algorithms (e.g., regression, classification, forecasting).

Nice To Haves

Direct experience with several of the following technologies: BigQuery, BigTable, Dataflow/Apache Beam, Dataproc, Pub/Sub or MQ, Spark, Starburst/Trino, MongoDB/CouchDB, Redis, Elastic
Experience with logging/monitoring stacks (Cloud Logging, Cloud Monitoring, error reporting, metrics dashboards
Experience with automated testing, data quality checks, monitoring for pipelines, and model governance such as drift, bias and anomaly detection
Experience with model development and operations technologies such as Vertex, Bedrock, Sagemaker, Jupyter, Hugging Face, TensorFlow, XGBoost, Anaconda, MLFlow, PyTorch, Scikit-learn
Experience with modelling techniques such as clustering, classification, logistic regression, natural language processing, neural networks, ensembling, computer vision, time-series analysis
Experience with data optimization and availability in generative AI solutions such as RAGs, knowledge graphs, MCPs, vectors, prompt validation and tuning environments

Responsibilities

Design and implement scalable, secure data pipelines from internal systems of record to Google Cloud Platform services (BigQuery, BigTable, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer).
Leverage, extend and advise on capability roadmaps for reusable frameworks and tooling (ingestion, transformation, quality, orchestration) actively being developed by the larger organization.
Enable self‑service data consumption and governance by standardizing patterns, templates, and sandbox capabilities rather than one‑off pipelines.
Design data architectures for training, validation and monitoring of predictive machine learning as well as generative AI solutions.
Define and implement standardized feature engineering and a common feature store with strong lineage, dictionary and high availability for models.
Optimize cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies).
Develop transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality checks, masking/tokenization routines).
Provide orchestration capabilities via Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration.
Implement robust data modeling (dimensional, data vault, or canonical models) and semantic layer implementations with BigQuery or similar tools.
Enforce data quality, lineage, and observability using standardized metrics, validation rules, and monitoring dashboards.
Partner with data scientists and domain solution teams to develop and deliver new model use cases onto GCP capabilities.
Document patterns, runbooks, and best practices, and provide enablement through workshops and code examples.