Onsite Human-Centered AI R&D Engineer (NLP & RAG)

Nova Southeastern University; Clearwater (FL); On-site; USD 55,000 - 96,000; Full time

Benefits offered by this job

Tuition waiver

Retirement plan

Excellent medical and dental plans

Job summary

Nova Southwestern University is seeking an AI Engineer R&D to support human-centered AI research in an onsite Clearwater, FL environment. The role focuses on building ML and LLM capabilities, designing evaluation strategies, and translating experiments into deployable systems with an interdisciplinary team. You will develop and optimize AI models, create RAG pipelines, and report findings to drive real-world AI deployments.

Qualifications

• Proficiency in Python or R and similar languages.
• Experience with AI/ML frameworks (TensorFlow, PyTorch, Hugging Face).
• Strong data analysis and statistical modeling knowledge.
• Experience with NLP and Retrieval-Augmented Generation (RAG).
• Excellent problem solving and attention to detail.
• Strong communication and teamwork abilities.

Responsibilities

• Research, build, train, and deploy ML models, algorithms, and LLM apps.
• Design custom workflows, including RAG pipelines.
• Test and evaluate model quality with benchmarks and feedback.
• Collaborate with interdisciplinary teams on human-centered AI solutions.
• Conduct experiments and analyze results to improve models.
• Contribute to AI system design and architecture.
• Optimize models for performance and scalability.
• Prepare technical reports and presentations on findings.
• Stay updated with AI research and propose new approaches.
• Ensure ethical and responsible use of AI technologies.

Skills

Python/R

ML frameworks

Data analysis

NLP/RAG

Problem solving

Communication

Education

Bachelor's Degree

Tools

TensorFlow

PyTorch

Hugging Face

Job description

Nova Southwestern University is seeking an AI Engineer R&D to support human-centered AI research in an onsite Clearwater, FL environment. The role focuses on building ML and LLM capabilities, designing evaluation strategies, and translating experiments into deployable systems with an interdisciplinary team. You will develop and optimize AI models, create RAG pipelines, and report findings to drive real-world AI deployments.