Productivity Engineer at Owens Corning focusing on maximizing asset utilization and executing productivity initiatives across manufacturing processes. Collaborating across teams to enhance efficiency and profitability.
About the role
- Prompt Engineer designing and optimizing LLM-driven solutions within Agentic POD architecture. Collaborating with AI engineers and data scientists to implement advanced prompting techniques.
Responsibilities
- Design, develop, and optimize prompts and prompt chains for LLM-based applications
- Work within Agentic POD architectures to enable seamless interaction between AI agents
- Optimize performance of LLMs such as Gemini, Claude, GitHub Copilot, and other foundation models
- Develop and implement prompt patterns (few-shot, chain-of-thought, ReAct, tool-augmented prompting)
- Build and enhance RAG (Retrieval-Augmented Generation) pipelines for accurate and context-aware responses
- Create reusable prompt templates and frameworks for enterprise-scale applications
- Improve engineering throughput by leveraging AI-assisted development workflows
- Evaluate and fine-tune prompts using LLM evaluation frameworks and metrics (accuracy, latency, cost)
- Collaborate with AI engineers, data scientists, and product teams to deliver optimized solutions
- Ensure responsible AI practices, bias mitigation, and prompt safety controls
- Continuously experiment with new prompting techniques and LLM capabilities
Requirements
- Overall 4+ years with 2-7 years of relevant work experience in Agentic AI Development
- B.Tech., M.Tech. or MCA degree from a reputed university
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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