Z
Artificial Intelligence Engineer
Zapnix LLC
2 hours ago
Full-time
On-site
AI Solution Development Engineer
Syracuse, NY – Onsite
(USC or GC only)
Full TimeJob Description & Skill Requirement:
Increase your chances of reaching the interview stage by reading the complete job description and applying promptly.
AI Solution Development & Deployment:
Design, develop, and deploy AI-driven solutions for engineering applications using frameworks such as Lang Graph 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. LLM Ops & Testing: Apply LLM Ops 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 latest AI trends, frameworks, and technologies to guide strategic adoption. Collaboration & Documentation: Work closely with multidisciplinary teams in a global environment. xsgimln Produce clear technical documentation and contribute to knowledge-sharing initiatives. "AI frameworks (Lang Graph or similar) Multi-cloud deployment (AWS, GCP, Azure) Containerization & orchestration (Docker, Kubernetes) LLM Ops best practices MCP integrations for agent-driven workflows Strong documentation and collaboration skills"
Increase your chances of reaching the interview stage by reading the complete job description and applying promptly.
AI Solution Development & Deployment:
Design, develop, and deploy AI-driven solutions for engineering applications using frameworks such as Lang Graph 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. LLM Ops & Testing: Apply LLM Ops 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 latest AI trends, frameworks, and technologies to guide strategic adoption. Collaboration & Documentation: Work closely with multidisciplinary teams in a global environment. xsgimln Produce clear technical documentation and contribute to knowledge-sharing initiatives. "AI frameworks (Lang Graph or similar) Multi-cloud deployment (AWS, GCP, Azure) Containerization & orchestration (Docker, Kubernetes) LLM Ops best practices MCP integrations for agent-driven workflows Strong documentation and collaboration skills"