Software Engineer, AI Experience

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About The Position

The AI Experience Engineer plays a critical role in advancing isolved's AI-Driven Development Lifecycle (AI-DLC), which is already in active use across engineering teams. This role operates at the intersection of developer experience and AI, focusing on improving how engineers interact with AI collaborators. By embedding directly with product teams, the engineer identifies workflow friction in real time, then designs and delivers targeted tooling and solutions to address it. This position emphasizes a hands-on, iterative approach-pairing with engineers in their day-to-day environments, diagnosing gaps between AI capability and actual adoption, and treating those gaps as solvable engineering problems. The role is instrumental in shaping AI-driven workflows, enhancing context and reliability, and ultimately enabling engineers to work more efficiently by reducing toil and increasing focus on high-value outcomes like system design and customer impact.

Requirements

4+ years of professional software engineering experience
Strong developer experience mindset, with the ability to identify friction and design scalable, user-focused solutions for internal engineers
Excellent collaboration and communication skills, with experience pairing effectively across varying levels of engineering expertise
Demonstrated product mindset for internal tooling, including measuring adoption and iterating based on user feedback
Hands-on experience using AI coding tools (e.g., Claude, Copilot, Cursor) as part of daily development workflows
Experience building LLM-powered applications, including prompt engineering, RAG, agentic workflows, and evaluation frameworks
Working proficiency in SQL Server, C#/.NET and ability to deliver production-quality solutions in a .NET ecosystem
Experience building or integrating tooling for agent-based workflows (e.g., MCP servers, tool integrations)
Familiarity with developer productivity metrics (e.g., DORA, DX Core 4) and applying them to drive measurable improvements

Responsibilities

Embed with product engineering teams to observe AI-DLC workflows, identify friction points, and translate them into actionable improvements
Partner directly with engineers through hands-on pairing to demonstrate effective AI-driven development practices
Design and build agentic workflows, including context-aware AI assistants, MCP servers, and internal copilots
Develop and maintain AI-driven development pipelines, including spec-driven workflows, AI-assisted code review, and automation agents
Own and evolve AI quality pipelines (e.g., risk analysis, test generation, failure triage) based on real-world usage and feedback
Build and maintain integrations connecting AI tools to engineering systems (e.g., codebase, IDP, tickets, runbooks)
Engineer context and retrieval systems to deliver the right information (code, docs, standards) into AI workflows at the right time
Develop and maintain reusable prompt libraries, ensuring continuous improvement as tooling and models evolve
Define and implement evaluation frameworks to measure impact on developer productivity and AI adoption (e.g., DORA, DX metrics)
Establish feedback loops to continuously refine tooling based on real engineering needs and observed friction
Ensure reliability, performance, and trustworthiness of AI tooling while collaborating with platform teams on shared infrastructure