AI Solution Development Engineer
Overview:
In this role you will design, develop, and deploy AI-driven solutions for engineering applications, embedding AI into internal tools to boost productivity and automation. You will collaborate with multidisciplinary teams to deliver scalable, secure AI capabilities across software, hardware, and mechanical engineering domains. You will work on cloud deployments, containerized architectures, and GI/LLM-driven workflows to improve reliability and efficiency. This is a hands-on role at the intersection of AI, engineering disciplines, and modern deployment practices. You will shape how AI accelerates engineering tasks and knowledge sharing in a global environment.
Responsibilities:
- Design, develop, and deploy AI-powered solutions for engineering workflows using LangGraph or similar frameworks
- Integrate AI capabilities into internal engineering tools to boost productivity and automation
- Deploy and manage AI solutions on AWS with focus on scalability, security, and cost optimization
- Implement containerization, orchestration (Kubernetes), and serverless architectures for AI workloads
- Support multi-cloud deployments across AWS, GCP, and AzureApply LLMOps practices for lifecycle management of large language models
- Develop and maintain CI/CD pipelines, monitoring, and governance for AI applications
- Design testing strategies to ensure reliability, accuracy, and compliance of AI applications
- Collaborate on MCP-based integrations and agent-driven AI workflows for engineering tools
- Enable AI-powered automation across software, hardware, and mechanical domains
- Evaluate and recommend commercial AI tools for engineering use cases
- Prepare technical documentation and contribute to knowledge-sharing initiatives
Key requirements:
- 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
- Knowledge of Docker, Kubernetes, and serverless architectures
- Experience with LLMOps, CI/CD pipelines, monitoring, and AI testing
- Familiarity with MCP integrations and agentic AI workflows
- Strong technical documentation and cross-functional collaboration skills
- Technical communicationproblem-solving
LangGraphAWSGCP