Job Description

• Develop and modify complex information system programs. Leads project teams and defines specifications for AI Model Development

• Design and implement end-to-end AI applications using LLMs, multimodal models, and classical ML where appropriate. Integrate foundation models (e.g., GPT-style models, open-source LLMs) into scalable, reliable software systems.

• Perform prompt engineering, fine-tuning, and adapter-based training (e.g., LoRA) to tailor models to domain-specific use cases. Evaluate trade-offs between fine-tuning vs. prompt-based vs. tool-augmented approaches.

• Develop multi-step and multi-agent workflows using LLM orchestration frameworks. Implement guardrails, fallbacks, and human-in-the-loop patterns.

• Collaborate with data engineers to build real-time, scalable data pipelines and deploy models into production environments using cloud platforms. Monitor model behavior, drift, hallucinations, and system performance in production.

• Ensure model transparency and fairness, mitigating bias and documenting model behaviors for regulatory compliance.

• Translate technical outputs into actionable business recommendations, presenting complex findings to non-technical executive leadership

• Read and follow the Underwriters Laboratories Code of Conduct, and follow all physical and digital security practices

• Performs other duties as directed.

• Develop and modify complex information system programs. Leads project teams and defines specifications for AI Model Development

• Design and implement end-to-end AI applications using LLMs, multimodal models, and classical ML where appropriate. Integrate foundation models (e.g., GPT-style models, open-source LLMs) into scalable, reliable software systems.

• Perform prompt engineering, fine-tuning, and adapter-based training (e.g., LoRA) to tailor models to domain-specific use cases. Evaluate trade-offs between fine-tuning vs. prompt-based vs. tool-augmented approaches.

• Develop multi-step and multi-agent workflows using LLM orchestration frameworks. Implement guardrails, fallbacks, and human-in-the-loop patterns.

• Collaborate with data engineers to build real-time, scalable data pipelines and deploy models into production environments using cloud platforms. Monitor model behavior, drift, hallucinations, and system performance in production.

• Ensure model transparency and fairness, mitigating bias and documenting model behaviors for regulatory compliance.

• Translate technical outputs into actionable business recommendations, presenting complex findings to non-technical executive leadership
• Read and follow the Underwriters Laboratories Code of Conduct, and follow all physical and digital security practices

• Performs other duties as directed.

• Advanced technical knowledge and/or software development experience.
• Advanced working knowledge in software application or specific program language requirements of software work.

• University degree in Computer Science or a related discipline.