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.
Qualifications
University degree in Computer Science or a related discipline.
58 years of professional experience in data science, machine learning, or AI application development.
23 years relevant experience in GenAI, LLM finetuning, multiagent workflows, and fairness/compliance in AI.
Handson with prompt engineering, adapterbased training (LoRA), and orchestration frameworks.
Advanced technical knowledge and/or software development experience.
Advanced working knowledge in software application or specific program language requirements of software work.
