Lead AI Engineer

Position Responsibilities:

• Design, develop, test, and deploy enterprise AI applications and services
• Develop scalable architectures that integrate AI capabilities into existing business systems and workflows
• Design and implement agentic AI patterns that can reason, retrieve enterprise data, trigger workflows, and support business actions
• Define reusable agent frameworks for priority use cases across in Supply Chain, ROP, Call Center, and Operations
• Mentor and coach developers, analysts, and technical team members on AI engineering best practices
• Collaborate with business stakeholders to understand challenges and identify AI opportunities
• Other projects or duties as assigned

Required Education and Experience:

• Bachelor's Degree in Computer Science, Engineering, Data Science, Information Technology, or related discipline with 4 to 6 years of experience in software engineering, data engineering, machine learning, or AI development or High School Diploma/General Education Degree (GED) with 7 to 9 plus years of specific experience
• 3+ years of hands-on experience implementing AI and machine learning solutions in production environments.
• Experience designing enterprise-scale applications and cloud-based architectures.
• Experience with Python, SQL, REST APIs, Git and DevOps practices, Machine Learning frameworks (TensorFlow, PyTorch, Scikit-Learn), Large Language Models (OpenAI, Azure OpenAI, Anthropic, Gemini, etc.), Prompt Engineering, and Retrieval-Augmented Generation (RAG)
• Experience with Vector Databases, Cloud Platforms (Azure, AWS, Google Cloud), CI/CD and Infrastructure Automation, Data Modeling and Data Engineering fundamentals, Microsoft Graph API, Cosmos DB or equivalent vector / operational data stores, GitHub Copilot and modern DevOps practices, and Snowflake or enterprise data platform integration

Preferred Education and Experience:

• Bachelor’s Degree
• Experience with the following:
• • Azure AI Foundry
  • Microsoft Copilot Studio
  • Microsoft Fabric
  • Snowflake
  • Databricks
  • LangChain
  • Semantic Kernel
  • Docker
  • Kubernetes
  • MLflow
  • LlamaIndex
  • Agent Frameworks and Autonomous AI Systems