AI Solution Development Engineer

Katalyst CRO; City of Syracuse (NY); On-site; USD 110,000 - 170,000; Full time

Job summary

Katalyst CRO seeks an AI Solution Development Engineer to design, develop, and deploy AI-driven solutions for engineering applications, integrating AI capabilities into internal tools to boost productivity and automation across software, hardware, and mechanical domains. The role requires experience with LangGraph, cloud AI services, AWS deployment, containerization, LLMOps, and agent-driven workflows.

You will collaborate with multidisciplinary teams to develop scalable, secure AI solutions,

Qualifications

• Experience designing, developing, and deploying AI-driven applications.
• Experience with LangGraph or similar AI frameworks.
• Experience deploying AI solutions on AWS or other cloud platforms.

Responsibilities

• Design, develop, and deploy AI-driven solutions for engineering applications.
• Integrate AI capabilities into internal engineering tools to improve productivity.
• Deploy and manage AI solutions on AWS; ensure scalability, security, and cost optimization.
• Implement containerization, orchestration, and serverless architectures for AI workloads.
• Develop MCP-based integrations and agent-driven workflows for internal tools.
• Collaborate with engineering teams to design and implement AI workflows and governance.

Skills

LangGraph

AI frameworks

AWS deployment

Docker

Kubernetes

Serverless architectures

LLMOps

CI/CD pipelines

AI application testing

MCP integrations

Agent-driven workflows

Cross-functional collaboration

Technical documentation

Education

BS in Computer Science or related field

Tools

AWS

GCP

Azure

Job description

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.