Job Summary: We are seeking an AI Solution Development Engineer to design, develop, and deploy AI-driven solutions for engineering applications. The role involves integrating AI capabilities into internal engineering tools to improve productivity, automation, and operational efficiency.
The ideal candidate will have experience with AI frameworks such as LangGraph, cloud AI services, AWS deployment, containerization, LLMOps, and agent-driven workflows. The candidate will collaborate with multidisciplinary engineering teams to develop scalable, secure, and reliable AI solutions across software, hardware, and mechanical engineering domains.
Roles & Responsibilities:
Design, develop, and deploy AI-driven solutions for engineering applications using LangGraph or similar frameworks.
Integrate AI capabilities into internal engineering tools to improve productivity and automation.
Develop scalable and reliable AI solutions for engineering workflows.
Deploy and manage AI solutions on AWS, ensuring scalability, security, and cost optimization.
Implement containerization, orchestration, and serverless architectures for AI workloads.
Support multi-cloud deployments across AWS, GCP, and Azure.
Apply LLMOps best practices for large language model lifecycle management
Develop and maintain CI/CD pipelines, monitoring systems, and governance processes.
Design and execute testing strategies to ensure AI application reliability, accuracy, and compliance.
Collaborate with engineering teams to design MCP-based integrations for internal tools.
Develop agent-driven workflows to streamline engineering processes.
Enable AI-powered automation across software, hardware, and mechanical engineering domains.
Evaluate and recommend commercial AI tools for engineering use cases.
Stay updated on emerging AI frameworks, technologies, and industry trends.
Support strategic AI technology adoption and continuous innovation.
Work with multidisciplinary teams in a global engineering environment.
Prepare clear technical documentation and implementation guidelines
Contribute to knowledge-sharing initiatives and engineering best practices.
Education:
A degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field is relevant; the source JD does not specify a mandatory degree requirement.
Experience:
Experience designing, developing, and deploying AI-driven applications.
Experience with AI frameworks such as LangGraph or similar technologies.
Experience deploying AI solutions on AWS or other cloud platforms.
Knowledge of Docker, Kubernetes, and serverless architectures.
Experience with LLMOps, CI/CD pipelines, monitoring, and AI application testing.
Familiarity with MCP integrations and agentic AI workflows.
Strong technical documentation and cross-functional collaboration skills.
Numbers & Facts
Location
Syracuse, NY
Skills
Amazon Web Services (AWS)unmatched
Artificial Intelligence (AI)unmatched
Automationunmatched
Automation Systemsunmatched
Best Practicesunmatched
Cloud Computingunmatched
Computer Engineeringunmatched
Computer Scienceunmatched
Continuous Deployment/Deliveryunmatched
Continuous Integrationunmatched
Cost Controlunmatched
Cross-Functionalunmatched
Data Scienceunmatched
Dockerunmatched
Engineering Softwareunmatched
GCP (Good Clinical Practices)unmatched
Industry/Trade Analysisunmatched
MCP - Microsoft Certified Professionalunmatched
Mechanical Engineeringunmatched
Microsoft Windows Azureunmatched
Modeling Languagesunmatched
Operational Strategyunmatched
Process Developmentunmatched
Productivity Managementunmatched
Scalable System Developmentunmatched
Software Engineeringunmatched
Team Playerunmatched
Technical Strategyunmatched
Technical Writingunmatched
Test Designunmatched
Test Strategyunmatched
Use Casesunmatched
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