AI Engineer — Agentic AI Platform

Flexhire is a stealth-mode startup building an operating system centered on AI agents, contextual intelligence, and wearable technology. The AI Engineer will architect and implement the agent layer, including runtime orchestration, LLM routing, memory retrieval, tool integrations, evaluation systems, and on-device inference optimization.

Responsibilities:

• The backend engineers build the infrastructure. The mobile engineers build the user facing surfaces. You build what runs between them — the agents themselves
• As our first AI Engineer you will own the design and implementation of our agent layer — the pipelines, reasoning chains, memory retrieval systems, tool integrations, and orchestration logic that turn raw LLM capability into a platform that genuinely replaces the app paradigm. You will work directly with the CEO and across the full engineering team to make sure the agent experience is as technically rigorous as it is experientially compelling
• This is a hands-on engineering role. You will write production code, own the agentic runtime architecture, and be directly accountable for the quality of every agent interaction on the platform. You will also be a key voice in decisions about which models to use, how to route between them, and how to structure the memory and context systems that make our platform smarter over time
• The platform agent runtime — the core orchestration layer that manages agent sessions, chains reasoning steps, routes to tools, and executes actions on behalf of users
• Multi-provider LLM integration and routing — selecting and switching between regional and task-specific language models dynamically, with latency, cost, and capability all factored into routing decisions
• RAG architecture and memory retrieval — the systems that give agents access to the user's persistent, encrypted context layer and make responses smarter and more relevant over time
• Tool and skill integration pipelines — the infrastructure that connects agents to external APIs, device capabilities, and first-party platform features
• Agent evaluation and observability — the frameworks that measure agent quality, surface failures, and give the team visibility into how agents are actually performing in production
• On-device inference optimization — working with the mobile and firmware teams to identify which parts of the agent pipeline can run locally on device, reducing latency and cloud dependency as the platform evolves toward wearable hardware
• Prompt architecture and system design — the structured prompting frameworks, system instructions, and context management patterns that govern agent behavior consistently across the platform