<p>Kforce has a client that is seeking an AI Engineer/Applied Data Scientist in Draper, UT.</p> <p>Overview: In this role, you will operate at the intersection of AI engineering and applied data science. You will design, build, and deploy machine learning, generative AI, and agentic AI systems that power real-world products and decision-making at scale.</p> <p>You will work end-to-end-from data exploration and modeling through production deployment-partnering closely with product, engineering, and business stakeholders to deliver measurable, reliable, and responsible AI outcomes.</p> <p>Duties Include:</p> <ul> <li>Design, build, and optimize machine learning models, including classification, regression, clustering, and recommendation systems </li><li>Develop and productionize LLM-based solutions, including prompt engineering, retrieval-augmented generation (RAG) pipelines, fine-tuning, and multimodal models </li><li>Build and orchestrate agentic AI workflows (LangGraph or similar), including tool usage, decision logic, and long-running agent execution </li><li>Leverage AI-assisted development tools (e.g., Claude Code or similar) to accelerate software development, testing, and refactoring while maintaining high standards of quality and correctness </li><li>Design and implement modular sub-agents and reusable tools, applying strong software engineering and data science principles across the agent lifecycle (design, build, evaluate, deploy, and iterate) </li><li>Apply embeddings and vector search techniques to enable NLP, semantic search, and retrieval use cases </li><li>Process and analyze large-scale datasets using Python (pandas, scikit-learn, PySpark) and SQL </li><li>Implement MLOps best practices, including CI/CD pipelines, model versioning, monitoring, evaluation, and reproducibility </li><li>Evaluate model and LLM performance in production using offline, online, and incremental evaluation strategies </li><li>Translate complex analytical results into clear, actionable insights for both technical and non-technical stakeholders* Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative field </li><li>2+ years of hands-on experience in data science, machine learning engineering, or applied AI within a fast-paced, production-oriented environment </li><li>Advanced proficiency in Python, including experience with pandas, scikit-learn, and PySpark </li><li>Strong SQL skills for large-scale data analysis and feature engineering </li><li>Proven experience building, tuning, and evaluating machine learning models, with a solid understanding of evaluation metrics and tradeoffs </li><li>Experience with vector embeddings, similarity search, and retrieval pipelines </li><li>Practical experience with LLMs, including prompt engineering, API/SDK integration, multimodal models, and fine-tuning approaches </li><li>Hands-on experience with agentic development frameworks (LangGraph preferred or equivalent), including orchestration patterns, sub-agents, and tool integration </li><li>Experience using AI-assisted (-agentic coding-) development tools, with strong engineering judgment around correctness, testing, and maintainability </li><li>Understanding of the agentic software lifecycle, including evaluation, observability, failure modes, and iterative improvement in production environments </li><li>Familiarity with responsible AI principles, including bias, fairness, and governance in deployed systems </li><li>Ability to translate business problems into scalable AI/ML solutions and communicate effectively across technical and non-technical audiences </li><li>Experience working cross-functionally in Agile environments, with clear and thorough documentation practices </li><li>Familiarity with model deployment and MLOps practices, including CI/CD, monitoring, and reproducibility </li></ul> <p>Nice to Have:</p> <ul> <li>Experience operating and scaling agentic AI systems in production environments </li><li>Background in recommendation systems, optimization, or decision intelligence </li></ul>