Principal AI Engineer

Candidates must not require sponsorship now or in the future.
TITLE Principal AI Engineer
COMPANY JTEKT DEPARTMENT Engineering
LOCATION Greenville, SC CLASSIFICATION Salaried (EXEMPT)
REPORTS TO Director Technical Center GRADE 107


Summary/Purpose
JTEKT North America is seeking a Principal AI Engineer to lead the design, architecture, and deployment of Generative AI and Machine Learning systems that power intelligent knowledge platforms, document understanding, and diagnostics. You’ll be responsible for building and orchestrating large language model (LLM)–driven solutions using Azure AI services, Retrieval-Augmented Generation (RAG) pipelines, and agentic frameworks.
In this role, you’ll not only build AI-powered user-facing tools but also architect robust systems for automating the ingestion and indexing of structured and unstructured enterprise data—including PDFs, SharePoint repositories, and sensor data. You’ll design and evaluate machine learning models for anomaly detection, diagnostics, and classification, applying both deep learning and classical techniques across engineering and operational domains.
As our lead GenAI architect, you’ll define the foundation for AI-powered applications across engineering, operations, and manufacturing—bridging cloud infrastructure, prompt workflows, and enterprise-scale data systems. This is a highly technical, hands-on role with strategic influence, designed for an individual who can both build and lead.

Essential Duties and Accountabilities:
This person may lead or assist in the activities listed but are not limited to the following:
• Architect and Deploy GenAI Systems: Design intelligent applications using Azure OpenAI, Prompt Flow, LangChain, Semantic Kernel, and other agentic frameworks.
• Develop and Orchestrate AI Pipelines: Integrate Azure services (Logic Apps, Container Apps, Cognitive Search, Azure Blob Storage, SharePoint) into end-to-end solutions that support retrieval, generation, and feedback workflows.
• Full-Stack AI Application Development: Build scalable backend services (Python, FastAPI, Node.js) and modern frontends (React, TypeScript, TailwindCSS) to deliver production-ready tools.
• Design and Optimize RAG Pipelines: Engineer semantic and hybrid search systems that power intelligent Q&A, summarization, and document extraction.
• Automate Data Ingestion and Indexing: Connect structured and unstructured enterprise data sources (PDFs, SharePoint, sensor signals) with AI systems.
• Implement and Evaluate ML Models: Apply deep learning and classical ML (e.g., CNNs, RNNs, XGBoost, logistic regression) for diagnostics, anomaly detection, and document classification.
• Apply Statistical Modeling: Use multivariate analysis, hypothesis testing, and Bayesian methods to build robust industrial analytics solutions.
• Establish Evaluation & Feedback Loops: Design systems to monitor LLM performance, detect hallucinations, and continuously improve relevance and output quality.
• Champion Responsible AI Practices: Ensure compliance with privacy, security, fairness, and transparency standards. Incorporate ethical considerations in AI design and deployment.
• Mentor and Scale: Provide technical mentorship and foster a collaborative, learning-oriented team culture as JTEKT’s AI capabilities grow.
• Align with Business Outcomes: Work cross-functionally with engineering, operations, IT, and innovation teams to ensure solutions drive real-world value and operational impact.
• Lead with Purpose: Help create systems that improve knowledge access, reduce decision-making friction, and accelerate enterprise-wide innovation.

Supervisory Responsibilities:
• This position has no supervisory responsibilities.
Job Knowledge, Skills and Abilities:
Ability to lead projects across:
• Backend: Python, FastAPI, Node.js
• Frontend: React, TypeScript, TailwindCSS
• Cloud & Infrastructure: Azure OpenAI, Cognitive Search, Prompt Flow, Logic Apps, Azure Blob Storage, SharePoint
• ML Frameworks: scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel
• Data & APIs: SQL, REST APIs, Pandas, NumPy

Expertise in:
• Large Language Models (LLMs), prompt engineering, retrieval tuning
• Agent orchestration, tool chaining, multi-agent coordination
• Deep learning (CNNs, RNNs, transformers) and classical ML models
• Statistical modeling: multivariate analysis, Bayesian inference
• Industrial data use cases: diagnostics, time-series forecasting, anomaly detection

Familiarity with:
• Human-in-the-loop feedback systems for LLM evaluation
• GitHub Actions, Docker, Kubernetes, CI/CD workflows

Education and Experience:
• E