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