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AI Solution Development Engineer (Mechanical Engineering)

Ztek ConsultingSyracuse, NY🇺🇸United StatesPosted Oct 14, 2026

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
Syracuse, NY, United States
Posted
22 hours ago
DockerAWSAzureComplianceGoogle CloudKubernetes

Job Description

Job Title: AI Solution Development Engineer (Mechanical Engineering) onsite in Syracuse, NY - Full-time

Salary: $120K per year

Job Description: 535926/BR

  • AI Solution Development & Deployment:
  • Design, develop, and deploy AI-driven solutions for engineering applications using frameworks such as LangGraph or similar.
  • Integrate AI capabilities into internal engineering tools to enhance productivity and automation.
  • Cloud AI Services Integration:
  • Deploy and manage AI solutions on AWS, ensuring scalability, security, and cost optimization.
  • Implement containerization, orchestration, and serverless architectures for AI workloads.
  • LLMOps & Testing:
  • Apply LLMOps best practices for lifecycle management of large language models, including CI/CD pipelines, monitoring, and governance.
  • Develop and execute testing strategies for AI applications to ensure reliability, accuracy, and compliance.
  • Agentic Workflows & MCP:
  • Collaborate with engineering teams to design MCP-based integrations for internal tools.
  • Enable agent-driven workflows that streamline engineering processes across software, hardware, and mechanical domains.
  • Technology Evaluation & Innovation:
  • Assess and recommend commercial AI tools for engineering use cases.
  • Stay informed about the latest AI trends, frameworks, and technologies to guide strategic adoption.
  • Collaboration & Documentation:
  • Work closely with multidisciplinary teams in a global environment.
  • Produce clear technical documentation and contribute to knowledge-sharing initiatives.

Skills:

  • AI frameworks (LangGraph or similar)
  • Multi-cloud deployment (AWS, Google Cloud Platform, Azure)
  • Containerization & orchestration (Docker, Kubernetes)
  • LLMOps best practices
  • MCP integrations for agent-driven workflows
  • Strong documentation and collaboration skills

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