Posted on: 03/03/2026
Responsibilities
- 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 :
- 5 to 8 years of professional experience in data science, machine learning, or AI application development.
- 23 years relevant experience in GenAI, LLM fine?tuning, multi?agent workflows, and fairness/compliance in AI.
- Advanced technical knowledge and/or software development experience.
- Advanced working knowledge in software application or specific program language requirements of software work.
- Hands?on with prompt engineering, adapter?based training (LoRA), and orchestration frameworks.
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