Applied AI Developer (Agent Evaluation

Position: Applied AI Developer (Agent Evaluation)
Position Overview
As a Software Developer on the Fusion platform services team within Product Development and Manufacturing Solutions (PDMS), you'll be part of a team of technologists dedicated to creating cutting-edge AI and generative AI solutions that enhance developer productivity and experience. You'll work closely with AI engineers, software architects, and product engineering teams to build and rigorously evaluate intelligent agentic systems — including benchmarking AI agents against commercial solvers — and develop MCP (Model Context Protocol)-based tooling that integrates seamlessly with IDEs such as VS Code and Cursor.

Responsibilities

Develop and orchestrate multi-agent AI systems for automated test generation, test execution, and end-to-end development workflow optimization using frameworks like Lang Graph, Auto Gen, or the Anthropic Agent SDK (Claude Code)

Design and implement agentic workflows that coordinate multiple AI agents to autonomously drive test automation across UI, API, integration, and system levels, from test case synthesis to result evaluation, ensuring seamless integration with existing developer tools and MCP-compatible services

Build evaluation frameworks and custom benchmarks for agentic systems, including comparisons of AI agents against commercial solvers, using tools like Agent Bench and Langfuse

Evaluate MCP server and tool performance across agentic pipelines, measuring latency, accuracy, context fidelity, and end-to-end task completion rates

Minimum Qualifications

BS/MS in Computer Science, Machine Learning, or a related applied AI field

Expertise in Python and ML frameworks (PyTorch, Transformers, scikit-learn)

Experience with Large Language Models applied to software understanding or test generation

Knowledge of AI evaluation methodologies and metrics for agentic task completion and test quality

Strong foundation in statistical analysis and experimental design

Experience with developer workflow and productivity measurement frameworks

Preferred Qualifications

Background in software engineering or QA with close collaboration with development teams

Familiarity with test automation frameworks (e.g., Playwright, Selenium, Pytest, Appium) and CI/CD pipelines

Experience designing benchmarks that compare AI agents against commercial or domain-specific solvers

Hands-on experience with MCP (Model Context Protocol), building, evaluating, and optimizing MCP servers and tool integrations within agentic pipelines

Experience with agentic AI frameworks including Lang Graph, Auto Gen, or the Anthropic Agent SDK / Claude Code

Knowledge in vision-language models or multi-modal AI for UI and system-level understanding and evaluation

Experience with Azure AI Foundry/ML or AWS cloud ML platforms

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