Lead Software Engineer - Research & Innovation Engineering

Wells Fargo Bank•Charlotte, NC

5d•Onsite

About The Position

About this role: Wells Fargo is seeking a hands-on, forward-thinking Lead Software Engineer to help build and scale a high-impact Research & Innovation Engineering practice focused on identifying, evaluating, and recommending emerging technologies —turning opportunities into measurable enterprise value. This is a unique opportunity to shape the technical foundation of an innovation pipeline that accelerates enterprise transformation through evidence-based adoption and investment decisions. In this role, you will design, build, and operate a secure, scalable, automated PoC Lab that enables rapid, parallel, and isolated evaluations of emerging technologies. You will codify standardized evaluation patterns, success criteria, and scoring mechanisms , and drive disciplined execution to deliver consistent, decision-grade outcomes. Because the technologies we evaluate are increasingly AI-enabled, this role is ideal for an engineer who thrives in fast-paced, ambiguous problem spaces and brings deep expertise in cloud-native engineering, automation, and modern AI/agent-based approaches to streamline PoC workflows and accelerate time-to-decision. You’ll play a critical role in validating vendor solutions, producing defensible findings, and informing high-stakes adoption and investment recommendations at the intersection of technology and business. In this role, you will: Design, build, and operate a secure, scalable, and modular PoC Lab / evaluation platform that supports multiple concurrent, isolated PoCs. Establish and evolve standardized evaluation patterns (templates, success criteria, test scenarios, scoring rubrics, and definition-of-done) to ensure consistent, decision‑grade outcomes across evaluations. Provision and maintain PoC environments to spec using Infrastructure-as-Code (IaC), reusable blueprints, and automation to minimize cycle time and maximize repeatability. Ensure enterprise-grade security, privacy, and compliance across all PoCs, including least-privilege access controls, data segregation, and audit-ready logging/traceability. Partner with technology, business, and vendor teams to define PoC goals, success criteria, and testing plans—ensuring technical feasibility and alignment to strategic outcomes. Partner with Enterprise Architecture, InfoSec, and Risk to validate PoC designs and ensure adherence to internal standards and regulatory expectations. Apply modern AI/ML and agent-based approaches to automate or accelerate PoC workflows (e.g., environment provisioning, test execution support, scoring assistance, and results analysis) given that evaluated technologies are increasingly AI-enabled. Build tooling that extracts insights from PoC outcomes and generates executive-ready summaries (e.g., “whitepaper-light” findings, ratings, and recommendations) for stakeholders and reuse. Maintain a centralized artifact and knowledge repository of PoC architectures, execution plans, evidence, and outcomes to drive transparency, comparability, and reuse across future evaluations. Provide technical input during vendor assessments and executive readouts to support adoption/investment recommendations grounded in evidence and measurable outcomes. Contribute to the evolution of an AI-assisted scouting and intake engine that streamlines evaluation intake, prioritization, and triage. Lead and mentor a small team of engineers—setting technical direction, driving execution, and ensuring high-quality outcomes.

Requirements

5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
3+ years of Proven ability to lead end-to-end PoC evaluations: translate vendor + internal requirements into scope, success criteria, test plans, execution, evidence, and clear recommendations.
3+ years of Hands-on experience working in cloud-native, containerized environments across public and private cloud (enough to evaluate fit, integration, operability, and risks).
3+ years experience with demonstrated experience partnering with a third-party lab/services providers to deliver PoCs—coordinating environment readiness, access/connectivity needs, support workflows, and execution cadence.
3+ years of strong foundation in enterprise security, privacy, and compliance expectations (least privilege, data segregation, audit-ready evidence, secure handling of credentials/secrets).
3+ working knowledge of Infrastructure-as-Code and automation (Terraform preferred) sufficient to enable repeatable environment setup/configuration and to validate vendor deployment patterns.
3+ years of strong Agile execution skills (sprint planning, iterative delivery, driving closure) in a fast-moving, cross-functional environment.
3+ years of Excellent technical writing and stakeholder communication skills to produce decision-ready artifacts (findings summaries, scoring inputs, recommendations).

Nice To Haves

Deep expertise designing and operating repeatable PoC/evaluation frameworks (standardized patterns, scoring rubrics, reusable test harnesses, artifact libraries).
Advanced Terraform/IaC capability (module design, policy-as-code, multi-environment patterns) and strong CI/CD or GitOps automation for repeatability.
Strong experience with AI-enabled technologies and modern AI/agent-based approaches, especially applied to evaluation acceleration (e.g., automating parts of analysis, evidence capture, reporting).
Experience evaluating SaaS and emerging vendors in regulated environments (integration, identity/auth, data handling, operational readiness, supportability).
Familiarity with enterprise governance touchpoints relevant to PoCs (e.g., third-party risk engagement patterns, security review readiness, audit-quality evidence practices).
Background in platform/infrastructure engineering (Kubernetes/OpenShift, networking, observability, identity) that enables sharper technical judgment during evaluations.
Experience producing executive-level readouts and “whitepaper-light” documentation that stands up to scrutiny (clear tradeoffs, risks, recommendation rationale).
Bachelor’s/Master’s in CS/Engineering (or equivalent experience).

Responsibilities

Design, build, and operate a secure, scalable, and modular PoC Lab / evaluation platform that supports multiple concurrent, isolated PoCs.
Establish and evolve standardized evaluation patterns (templates, success criteria, test scenarios, scoring rubrics, and definition-of-done) to ensure consistent, decision‑grade outcomes across evaluations.
Provision and maintain PoC environments to spec using Infrastructure-as-Code (IaC), reusable blueprints, and automation to minimize cycle time and maximize repeatability.
Ensure enterprise-grade security, privacy, and compliance across all PoCs, including least-privilege access controls, data segregation, and audit-ready logging/traceability.
Partner with technology, business, and vendor teams to define PoC goals, success criteria, and testing plans—ensuring technical feasibility and alignment to strategic outcomes.
Partner with Enterprise Architecture, InfoSec, and Risk to validate PoC designs and ensure adherence to internal standards and regulatory expectations.
Apply modern AI/ML and agent-based approaches to automate or accelerate PoC workflows (e.g., environment provisioning, test execution support, scoring assistance, and results analysis) given that evaluated technologies are increasingly AI-enabled.
Build tooling that extracts insights from PoC outcomes and generates executive-ready summaries (e.g., “whitepaper-light” findings, ratings, and recommendations) for stakeholders and reuse.
Maintain a centralized artifact and knowledge repository of PoC architectures, execution plans, evidence, and outcomes to drive transparency, comparability, and reuse across future evaluations.
Provide technical input during vendor assessments and executive readouts to support adoption/investment recommendations grounded in evidence and measurable outcomes.
Contribute to the evolution of an AI-assisted scouting and intake engine that streamlines evaluation intake, prioritization, and triage.
Lead and mentor a small team of engineers—setting technical direction, driving execution, and ensuring high-quality outcomes.