Senior Specialty Software Engineer (AI Engineering)

Wells Fargo & Company•Concord, CA

About The Position

About this role: Wells Fargo is seeking a Specialty Senior Software Engineer – AI Engineering within the Digital Technology & Innovation organization. This role plays a key part in developing the self‑service MLOps framework, creating reusable AI engineering components, and enabling both real‑time and batch inferencing for enterprise-scale AI applications. Expected to be hands‑on engineering, cloud/on‑prem integration, agentic AI frameworks, and modern AI development practices. In this role, you will: AI Engineering, Frameworks & TPOps Development Design, enhance, and maintain the Tachyon Predictive Ops (TPOps) self‑service MLOps framework, enabling rapid experimentation, training, deployment, and monitoring of AI models. Build cloud‑native MDLC (Model Development Lifecycle) capabilities including model registry, versioning, lineage, and reproducibility. Develop unified libraries, SDKs, and extensible components that accelerate both predictive and generative AI workflows. Implement reusable automation patterns for model training, validation, deployment, and governance. Platform Engineering & Hybrid AI Enablement Contribute to the Unified & Managed Predictive AI Platform, spanning on‑premise infrastructure, GCP, and upcoming Azure ML integration. Implement real‑time and batch inferencing capabilities supporting instant prediction use cases and scheduled batch pipelines. Support hybrid AI delivery patterns—predictive ML, GenAI workflows, agentic systems, and multi‑agent orchestration. Observability & Enterprise Governance Build strategic observability features including drift detection, performance optimization, and open-standards monitoring integrations. Collaborate with MLOps, platform engineering, and architecture to ensure compliance with enterprise governance and operational excellence requirements. API, Services & Tooling Development Design and develop scalable APIs and microservices to expose AI capabilities to enterprise applications. Implement automation and CI/CD patterns enabling consistent deployments across hybrid compute environments. Develop prompt engineering standards and reusable blueprints for LLM‑powered developer tools such as Copilot, Devin.AI, and agentic systems. Collaboration & Influence Work with data scientists, engineers, and platform teams to integrate AI pipelines and frameworks across the enterprise. Provide hands-on guidance to junior engineers on modern AI engineering and platform development patterns.

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
3+ years hands-on experience with AI/ML development and modern ML frameworks.
2+ years strong programming skills in Python and/or Java.
2+ years in building APIs, frameworks, automation pipelines, or distributed systems.
2+ years experience with cloud platforms (GCP, Azure, AWS) or on‑prem platforms such as Kubernetes or OpenShift.

Nice To Haves

Experience developing MLOps frameworks, model lifecycle automation, or self‑service developer platforms.
Experience with Vibe Coding, coding with AI assistance patterns, or integrating AI co‑developers into software engineering workflows
Experience with GenAI and agentic frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.
Strong understanding of MDLC components like model registry, versioning, monitoring, and reproducibility.
Experience with real‑time inferencing systems, event-driven architectures, or high-throughput pipelines.
Familiarity with cloud-native AI toolchains (Vertex AI, Azure ML) and observability stacks.
Excellent analytical, problem‑solving, and communication skills.

Responsibilities

AI Engineering, Frameworks & TPOps Development Design, enhance, and maintain the Tachyon Predictive Ops (TPOps) self‑service MLOps framework, enabling rapid experimentation, training, deployment, and monitoring of AI models.
Build cloud‑native MDLC (Model Development Lifecycle) capabilities including model registry, versioning, lineage, and reproducibility.
Develop unified libraries, SDKs, and extensible components that accelerate both predictive and generative AI workflows.
Implement reusable automation patterns for model training, validation, deployment, and governance.
Contribute to the Unified & Managed Predictive AI Platform, spanning on‑premise infrastructure, GCP, and upcoming Azure ML integration.
Implement real‑time and batch inferencing capabilities supporting instant prediction use cases and scheduled batch pipelines.
Support hybrid AI delivery patterns—predictive ML, GenAI workflows, agentic systems, and multi‑agent orchestration.
Build strategic observability features including drift detection, performance optimization, and open-standards monitoring integrations.
Collaborate with MLOps, platform engineering, and architecture to ensure compliance with enterprise governance and operational excellence requirements.
Design and develop scalable APIs and microservices to expose AI capabilities to enterprise applications.
Implement automation and CI/CD patterns enabling consistent deployments across hybrid compute environments.
Develop prompt engineering standards and reusable blueprints for LLM‑powered developer tools such as Copilot, Devin.AI, and agentic systems.
Work with data scientists, engineers, and platform teams to integrate AI pipelines and frameworks across the enterprise.
Provide hands-on guidance to junior engineers on modern AI engineering and platform development patterns.