Summary
A technology-focused organization is seeking a Lead AI Engineer to define the architecture and engineering practices behind sophisticated multi-agent AI systems. You will bridge AI research and production engineering by designing orchestration, memory, tool-use, evaluation, safety, and reliability mechanisms. The role requires 5+ years of software engineering leadership and strong experience with backend or distributed systems, along with hands-on experience building agent frameworks and production AI services.
Highlights
Lead the technical vision for advanced AI agent systems, shape production-grade architecture and engineering standards, and guide teams building reliable autonomous workflows for enterprise environments.
Description
We are looking for a Tech Lead to own the technical vision, architectural framework, and engineering rigor behind our multi-agent AI systems.
In this role, you will bridge cutting-edge AI research with production-grade software engineering—defining how autonomous agents plan, orchestrate, and reliably integrate into enterprise environments.
Key Responsibilities
Agent Architecture & Planning: Design robust multi-agent frameworks capable of complex reasoning, goal decomposition, autonomous execution, and self-correction.Orchestration & Workflow Control: Architect state-management pipelines, memory architectures, and routing mechanisms to orchestrate complex tool-use and multi-step agent workflows.Production Integration: Own the end-to-end integration of agentic systems into core infrastructure, ensuring high availability, low latency, and deterministic reliability.Engineering Standards: Set production-grade benchmarks for AI safety, guardrails, evaluation metrics, unit testing, and CI/CD pipelines specific to non-deterministic systems.Technical Leadership: Guide engineering teams on best practices, conduct architectural reviews, and maintain code quality across all AI-driven services.What We’re Looking For
Technical Depth: 5+ years of software engineering leadership, with proven experience building and deploying complex backend systems or distributed architectures.Agentic AI Focus: Hands-on experience building agent frameworks (e.g., LangGraph, AutoGen, CrewAI, or custom orchestration platforms) utilizing LLM tool-calling and memory stores.Production Rigor: Deep knowledge of API design, microservices, vector databases, and real-time streaming architectures.Standards & Evaluation: Experience setting up evaluation frameworks (evals) and guardrails for non-deterministic model outputs.this will be a B2B engagement
