Job Description

• Design, build, and deploy AI agents and end-to-end agentic AI solutions that solve complex problems across multiple technical and manufacturing domains.
• Work directly with domain PhDs and subject-matter experts to understand, document, and translate complex scientific and manufacturing processes into practical AI workflows.
• Translate domain expertise and process knowledge into structured workflows, agent architectures, tools, models, and production-ready agentic systems.
• Develop agentic AI solutions using frameworks such as LangGraph, LangChain, Semantic Kernel, or other agent orchestration frameworks.
• Build multi-agent workflows, RAG pipelines, tool-calling agents, memory-enabled agents, and autonomous/semiautonomous workflows grounded in real-world domain knowledge.
• Integrate agents with enterprise systems, APIs, databases, and specialized tools to enable real-world task execution and decision-making.
• Deploy, monitor, troubleshoot, and continuously optimize agentic AI solutions in production, with appropriate security, governance, reliability, and scalability.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Strong programming skills in Python with hands-on experience developing production-grade AI applications.
• Proven hands-on experience building and deploying Agentic AI systems in real-world production environments.
• Strong experience with agent frameworks such as LangGraph, LangChain, Semantic Kernel, or equivalent frameworks, with a deep understanding of agent orchestration and workflow design.
• Direct experience developing multi-agent systems, tool-calling agents, autonomous/semiautonomous workflows, RAG-based agents, agent memory, and agent-to-agent interactions.
• Experience designing and implementing real-world agent workflows involving tool integration, API calls, decision-making, planning, state management, human-in-the-loop processes, and workflow orchestration.
• Experience building LLM-based applications and integrating LLMs with enterprise data, APIs, databases, and external tools.
• Ability to work closely with technical domain experts and translate specialized scientific/process knowledge into effective agentic systems.
• Strong analytical, problem-solving, and process-mapping skills, with the ability to understand and work across unfamiliar technical domains.
• Experience deploying, monitoring, evaluating, and optimizing production agentic AI systems, including observability, reliability, security, governance, and MLOps practices.