AI Tool Developer

AI Tool Developer Plano, TX The AI Tool Development Support will primarily be responsible for collaborating and providing technical support. Responsibilities: Develop an end-to-end AI model and processing pipeline that transforms natural-language or structured test specifications into executable JSON scripts used by internal test automation tool. Build prompt-driven, rule-augmented, or fine-tuned models using TMNA-approved AI platforms, ensuring compliance with internal AI/ML Review Board standards. Create data ingestion and training datasets using historical test cases, existing automation scripts, and test execution logs. Design the mapping framework between spec semantics → automation actions → JSON schema. Build validation utilities to automatically ensure script correctness, schema compliance, and alignment with automation tool capabilities. Integrate the AI generation pipeline into the existing test automation toolchain and CI-based workflows. Collaborate with automation SMEs to refine domain-specific rules, edge cases, and test coverage requirements. Drive continuous model improvement through error analysis, incremental fine-tuning, and user feedback loops. Maintain documentation, versioning, and traceability of AI-generated scripts. Ensure responsible AI usage by aligning with TMNA governance, data-handling, and compliance requirements. Skills Required: 5+ years of experience building AI/ML or NLP-based solutions, including prompt engineering, LLM-based workflows, or model fine-tuning. Solid Python or similar development skills (data processing, model building, evaluation pipelines). Experience working with JSON schema design and automated test frameworks. Familiarity with ML frameworks such as PyTorch, TensorFlow, HuggingFace or similar libraries. Experience with building or integrating ML pipelines into production-quality tools (API services, microservices, or batch systems). Strong understanding of software testing concepts: test cases, assertions, conditions, flows, automation logic. Ability to collaborate with cross-functional engineering teams and translate ambiguous test definitions into structured logic. Educational Background: Bachelor's Degree (or higher) in Computer Science, Management Information Systems or related discipline, or equivalent professional work experience Added Bonus: Experience with automated test frameworks used in embedded or multimedia systems. Experience with model fine-tuning using enterprise datasets. Background in building developer tooling, code generation, or compiler/AST-type transformations. Familiarity with LEAP/MM automation concepts or previous exposure to internal test automation pipelines. Experience implementing AI tools in enterprise environments requiring governance and model approval.

Created: 2026-03-04