Forward Deployed Engineer I/II, AI Enablement

Washington Trust Bank•Spokane, WA

9h•$87,093 - $149,745

About The Position

The Forward Deployed Engineer, AI Enablement partners with business areas across the Bank to identify, design, build, document, and improve AI-enabled workflows and capabilities that make it easy to Sell, Service, and Scale the company. This role helps translate business needs into practical solutions by combining business process understanding, data literacy, AI fluency, stakeholder communication, and lightweight technical implementation. The Forward Deployed Engineer works with business users, process owners, technology, data, security, risk, and governance partners to develop safe, useful, and reusable AI-enabled tools, templates, workflows, documentation, and service patterns. This position requires curiosity, critical thinking, creativity, strong follow-through, and comfort operating in work that may not be fully defined at the start.

Requirements

Demonstrated ability to understand complex business processes, identify high-value improvement opportunities, and translate them into practical technology-enabled solutions.
Experience designing, building, testing, and operationalizing AI-enabled workflows, agents, automations, applications, reporting, or decision-support capabilities.
Working knowledge of agentic AI concepts, including instructions, tool use, structured outputs, contextual grounding, retrieval, human review, exception handling, orchestration, and evaluation.
Proficiency with at least one modern programming or scripting language, such as Python, C#, JavaScript, or TypeScript, with the ability to learn and apply new technologies quickly.
Experience using agentic and command-line development workflows—including tools such as GitHub Copilot and Codex-style coding agents—to plan, generate, inspect, test, debug, and refine solutions.
Ability to direct coding agents through clear goals, context, constraints, acceptance criteria, and iterative review while maintaining human accountability for the resulting solution.
Experience integrating systems and data through APIs, connectors, automation platforms, relational databases, analytical platforms, or event-driven patterns.
Ability to build small, testable solutions, validate them directly with users, and iteratively improve reliability, usability, workflow fit, and measurable business value.
Strong data fluency, including experience with structured and unstructured data, data models, reporting, analytics, and the context required to ground AI solutions.
Experience within Microsoft’s enterprise technology ecosystem, which may include Microsoft Fabric, Azure AI Foundry, Microsoft 365 Copilot, Copilot Studio, GitHub Copilot, Power BI, Power Platform, Azure, and related services.
Understanding of production-readiness considerations, including permissions, security, monitoring, error handling, documentation, support ownership, change management, and operational controls.
Ability to work across business, operations, technology, data, security, risk, compliance, and governance teams in a regulated enterprise environment.
Strong written, verbal, documentation, and facilitation skills, including the ability to create clear technical and operational materials, embed with business teams, learn unfamiliar domains quickly, explain technical concepts clearly, and navigate ambiguity.
Sound judgment in determining when AI is appropriate and when deterministic processes, traditional automation, reporting, data, or human-owned solutions would be more effective.
A builder mindset characterized by curiosity, adaptability, ownership, practical problem-solving, accountability for measurable outcomes, and the ability to convert successful solutions into reusable components and patterns.
Demonstrated self-motivation, initiative, attention to detail, and organizational ability; works effectively both independently and collaboratively.
Ability to prioritize multiple assignments, manage interruptions, and maintain a high level of service in a deadline-driven environment.
Ability to work additional hours as required by operational and production workloads.

Nice To Haves

Banking or financial-services experience is preferred.

Responsibilities

Embeds with business teams and process owners to understand how work moves end to end across people, processes, systems, data, decisions, controls, handoffs, and exception paths.
Identifies high-value opportunities to reduce cognitive, context, coordination, execution, quality, or design burden.
Determines whether AI, deterministic automation, data capability, reporting, process redesign, or human judgment is the appropriate response.
Designs future-state workflows with clear ownership, human and AI responsibilities, review points, exception handling, data boundaries, system interactions, and control requirements.
Designs, builds, and validates working AI-enabled solutions that combine agents, approved data, APIs, automation, reporting, workflow components, and human review into cohesive business capabilities.
Develops agent instructions, system prompts, tool-use patterns, structured outputs, context and retrieval approaches, evaluation criteria, error handling, and escalation paths appropriate to the use case.
Uses programming, scripting, agentic development workflows, APIs, approved platforms, and automation tools to build, inspect, test, debug, and refine solutions.
Builds small, testable versions; validates them with domain experts and users; and iterates based on workflow fit, output quality, reliability, usability, adoption, and business value.
Advances successful prototypes toward operational use by defining production-readiness requirements, runbooks, monitoring expectations, service ownership, permissions, support paths, change requirements, and control points with appropriate partners.
Documents assumptions, decisions, limitations, risks, dependencies, evaluation results, support requirements, and handoff information so capabilities can be reviewed, maintained, supported, and reused.
Measures outcomes such as reduced cycle time, fewer handoffs, less rework, improved consistency, stronger decision context, increased operating capacity, adoption, governance confidence, and reuse.
Converts successful local solutions into reusable agents, modules, templates, data or context assets, playbooks, evaluation patterns, and service models that accelerate future work.
Identifies when an AI-enabled approach is inappropriate and recommends deterministic process, data, reporting, automation, or human-owned alternatives.
Escalates data, security, privacy, risk, compliance, governance, production, integration, or supportability concerns early and works with the appropriate owners to resolve or document them.