A SaaS? platform that connects LLM? agents to internal APIs?, databases, and SaaS? tools with testing, permissions, and production monitoring built in.
Added Jun 5, 2026
Companies are moving from simple LLM? features to agentic workflows that take actions inside real products and internal systems. Engineering teams struggle to safely orchestrate tool calling, RAG?, framework-specific agents, API? access, evaluation, and production reliability across heterogeneous stacks.
Provide a control plane for building and operating agentic workflows across LangChain, LangGraph, CrewAI, direct LLM? APIs?, internal APIs?, databases, and third-party SaaS? tools. The product would offer managed connectors, permission scopes, test harnesses, evals, trace logs, rollback controls, and production monitoring for autonomous actions.
Multiple companies are hiring specifically for production LLM? agents, tool/function calling, and AI workflow integration, indicating the work has moved beyond experiments into product and platform infrastructure needs.
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Work with LLM APIs, agent orchestration, RAG, tool calling, workflow automation and external API integrations where applicable. Own features end-to-end, from requirement understanding and system design to development, testing, deployment and production optimisation.
Lead end-to-end technical delivery of AI agent and LLM-powered solutions: discovery, scoping, system design, PoC, production rollout, and operational handoff. Build production-grade integrations and custom connectors (APIs, data pipelines, RAG systems) to surface high-quality signals into Zendesk AI workflows.
* Prototype GenAI and Agentic AI solutions, including prompt/context engineering and evaluation. * Integrate LLMs via APIs, function/tool calling, and structured outputs; test and validate AI agents that automate or augment business workflows.
Design platform capabilities for agent execution, tool calling, streaming, state management, persistence, and long-running workflows. Build resilient integrations with multiple LLM providers and model-serving platforms.
Build agentic workflows. Create and run the orchestration layers and secure runtime environments for LLM tool-use. Bridge information silos. Build out the enterprise knowledge base and connectors that let agents reason across disparate data sources.
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