AI Engineer

Kforce has a client in Draper, UT that is seeking an AI Engineer who will operate at the intersection of AI engineering and applied data science. The Engineer will design, build, and deploy machine learning, generative AI, and agentic AI systems that power real-world products and decision-making at scale.Duties:Design, build, and optimize machine learning models, including classification, regression, clustering, and recommendation systemsDevelop and productionize LLM-based solutions, including prompt engineering, retrieval-augmented generation (RAG) pipelines, fine-tuning, and multimodal modelsBuild and orchestrate agentic AI workflows (LangGraph or similar), including tool usage, decision logic, and long-running agent executionLeverage AI-assisted development tools (e.g., Claude Code or similar) to accelerate software development, testing, and refactoring while maintaining high standards of quality and correctnessDesign and implement modular sub-agents and reusable tools, applying strong software engineering and data science principles across the agent lifecycle (design, build, evaluate, deploy, iterate)Apply embeddings and vector search techniques to enable NLP, semantic search, and retrieval use casesProcess and analyze large-scale datasets using Python (pandas, scikit-learn, PySpark) and SQLImplement MLOps best practices, including CI/CD pipelines, model versioning, monitoring, evaluation, and reproducibilityEvaluate model and LLM performance in production using offline, online, and incremental evaluation strategiesTranslate complex analytical results into clear, actionable insights for both technical and non-technical stakeholdersStay current with emerging trends in AI, ML, generative AI, and agentic systems, and apply them pragmatically to business challenges* Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative field2+ years of hands-on experience in data science, machine learning engineering, or applied AI within a fast-paced, production-oriented environmentAdvanced proficiency in Python, including experience with pandas, scikit-learn, and PySparkStrong SQL skills for large-scale data analysis and feature engineeringProven experience building, tuning, and evaluating machine learning models, with a solid understanding of evaluation metrics and tradeoffsExperience with vector embeddings, similarity search, and retrieval pipelinesPractical experience with LLMs, including prompt engineering, API/SDK integration, multimodal models, and fine-tuning approachesHands-on experience with agentic development frameworks (LangGraph preferred or equivalent), including orchestration patterns, sub-agents, and tool integrationExperience using AI-assisted (-agentic coding-) development tools, with strong engineering judgment around correctness, testing, and maintainabilityUnderstanding of the agentic software lifecycle, including evaluation, observability, failure modes, and iterative improvement in production environmentsFamiliarity with responsible AI principles, including bias, fairness, and governance in deployed systemsAbility to translate business problems into scalable AI/ML solutions and communicate effectively across technical and non-technical audiencesFamiliarity with model deployment and MLOps practices, including CI/CD, monitoring, and reproducibilityNice to Have:Experience operating and scaling agentic AI systems in production environmentsBackground in recommendation systems, optimization, or decision intelligenceExperience building and delivering AI-powered products (beyond prototyping or research environments)