60 Zeus Manufacturing Engineering Manager Jobs Hiring Near You

Job Summary

This position will be responsible for designing, developing, and operationalizing enterprise AI solutions that enable intelligent automation, conversational AI, AI agents, and scalable AI-powered workflows across manufacturing and enterprise business functions. This role focuses on transforming AI concepts into production-ready systems by integrating Large Language Models (LLMs), AI agents, enterprise data platforms, APIs, orchestration frameworks, and business applications. The AI Engineer will work closely with data scientists, data engineering, enterprise architecture, manufacturing teams, and business stakeholders to deploy scalable AI capabilities aligned with Zeus's enterprise AI strategy. The ideal candidate possesses strong software engineering and AI integration experience, including hands-on expertise with AI orchestration frameworks, APIs, vector search, retrieval-augmented generation (RAG), and enterprise system integration. This individual will play a critical role in establishing enterprise AI standards, reusable AI patterns, and operational AI platforms that support future AI scale across the organization.

Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. 

Minimum Qualifications: 

• Bachelor's degree in Computer Science, Software Engineering, Data Science, AI, or related technical discipline. 
• 6+ years of experience in software engineering, AI engineering, machine learning engineering, or related technical roles. 
• Strong proficiency in Python and experience developing APIs, backend services, and automation frameworks. 
• Hands-on experience with LLMs, prompt engineering, AI agents, RAG architectures, and vector databases/search technologies. 
• Experience with modern AI frameworks and orchestration tools (LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar). 
• Experience integrating enterprise systems using REST APIs, event-driven architectures, or middleware platforms.
•  Familiarity with cloud and data platforms such as Snowflake, Azure, AWS, Databricks, or similar ecosystems. 
• Strong understanding of software engineering best practices including Git, CI/CD, testing, monitoring, and deployment pipelines. 
• Excellent communication and cross-functional collaboration skills. Preferred Qualifications 
• Master's degree in Computer Science, Software Engineering, Data Science, AI, or related technical discipline. 
• Experience with Snowflake Cortex, Snowpark, AI agent frameworks, or enterprise AI platforms. 
• Experience with enterprise knowledge graphs, semantic layers, or document intelligence systems. 
• Familiarity with MCP architectures, AI orchestration platforms, or multi-agent systems. 
• Experience supporting AI deployments in manufacturing or industrial environments.
• Demonstrated ability to operationalize AI solutions from prototype through enterprise deployment. 

• Design, develop, and deploy enterprise AI applications, AI agents, copilots, and intelligent workflow solutions across manufacturing and corporate domains. 
• Build and maintain AI orchestration pipelines utilizing LLMs, retrieval-augmented generation (RAG), vector search, prompt frameworks, and agent-based architectures.
•  Develop scalable AI integration patterns between enterprise platforms such as Snowflake, Salesforce, ERP systems, SCADA/OT systems, document repositories, and collaboration platforms.
•  Collaborate with data scientists and business stakeholders to operationalize machine learning and generative AI solutions into production environments. 
• Design APIs, middleware, and integration services supporting AI-driven automation and cross platform communication. 
• Develop and optimize semantic search, enterprise knowledge retrieval, and conversational AI capabilities.
• Support the implementation of AI governance, security, observability, model evaluation, and human-in-the-loop approval processes. 
• Drive enterprise AI architecture strategy and standards, partnering across teams to define approaches for AI platforms, orchestration, deployment, and lifecycle management. 
• Data Security in workflows, Prompt injection Mitigation, Model explainability (where required) 
• Monitor AI application performance, cost efficiency, reliability, and user adoption metrics. 
• Stay current on emerging AI technologies, frameworks, and industry trends, and evaluate their applicability to enterprise AI initiatives. 
• Understand and optimize consumption and cost patterns