Agentic AI Business Applications Developer

Key Responsibilities

• Design the interface layer connecting AI tools and agents to business systems, selecting the right access pattern for each project.
• Make clear, reasoned decisions about live retrieval vs. caching vs. staging vs. structured storage — and own those decisions.
• Implement and maintain MCP-based capability interfaces and callable skills with explicit inputs, outputs, and scopes.
• Design clean, typed request/response contracts optimized for how AI agents reason and respond.
• Design and run evaluation frameworks measuring accuracy, relevance, groundedness, and hallucination rates.
• Diagnose root causes when AI tools produce poor or inaccurate responses, tracing failures to interface design, retrieval strategy, or data quality.
• Establish quality standards across every capability the team builds and continuously improve based on observed outcomes.
• Build production-quality documentation for every capability, endpoint, and integration.
• Develop reusable patterns and frameworks that make building new capabilities faster and more consistent.
• Train team members on AI-friendly system design, capability decomposition, and security considerations.
• Stay current with the evolving AI tooling and agent ecosystem and bring relevant advancements back to the team.

Preferred Qualifications

• Understanding and experience in systems design, API development, platform engineering, or solutions architecture.
• Experience integrating with enterprise business platforms such as analytics tools, collaboration systems, and relational databases.
• Working knowledge of RBAC, SSO, and governed access control in enterprise environments.
• Python and SQL proficiency — comfortable querying, transforming, and integrating data from enterprise systems.
• Hands-on experience designing and documenting APIs (OpenAPI, JSON Schema, REST, GraphQL) in production.
• Active daily use of modern AI developer tools as a builder — using these tools to build things, not just ask questions.
• Experience implementing or configuring MCP servers or AI agent tool/skill definitions.
• Experience designing AI evaluation frameworks (hallucination, relevance, groundedness).
• Background in semiconductor, hardware, or high-tech manufacturing — familiarity with engineering data, supply chain, PLM, or EDA systems is a plus.
• Familiarity with RAG architectures and how retrieval design affects AI output quality.
• Experience in a business-embedded, non-IT team making independent integration and architecture decisions.