Lead Software Engineer - AI Agents

Leads development of scalable, low-latency backend systems that power production AI agents in real-time enterprise environments. The role requires 5+ years of backend, distributed-systems, or platform-engineering experience, with AI agent and LLM experience strongly preferred.

About the job

Responsibilities

Design and build scalable backend systems powering AI agents in real-time enterprise environments.
Develop agent orchestration frameworks for multi-step reasoning, tool usage, and decisioning workflows.
Build systems for agent memory, context management, and state persistence across interactions.
Architect low-latency inference pipelines integrating LLMs, SLMs, and external tools and services.
Implement evaluation frameworks to measure agent performance, accuracy, and reliability.
Enable continuous improvement loops from feedback through retraining and deployment for production AI agents.
Design and manage event-driven, asynchronous workflows for complex agent tasks.
Optimize systems for high throughput, low latency, and cost-efficient inference at scale.
Build and maintain robust REST and gRPC APIs and service layers for agent capabilities.
Partner with Applied AI and ML teams to productionize models and agent behaviors.
Collaborate with Product and Solutions teams to translate customer workflows into agentic systems.
Drive best practices in observability, monitoring, safety, and guardrails for AI systems.
Contribute to architecture decisions for scaling multi-tenant, enterprise-grade AI platforms.

Requirements

5+ years of experience in backend engineering, distributed systems, or platform engineering.
Strong experience building high-scale, production-grade backend systems.
Experience designing systems for real-time processing, streaming, or event-driven architectures.
Strong understanding of API design, including REST and gRPC, and microservices architectures.
Experience with SQL and NoSQL databases and data modeling for high-scale systems.
Hands-on experience with Docker, Kubernetes, and cloud platforms such as AWS, Google Cloud, or Azure.
Strong fundamentals in system design, concurrency, and performance optimization.

Nice-to-Haves: Agent and AI

Experience working with LLMs, conversational AI, or AI-powered products in production.
Familiarity with agent frameworks, tool calling, or multi-step reasoning systems.
Experience building or integrating RAG pipelines, vector databases, or retrieval systems.
Exposure to offline and online evaluation systems and A/B testing for AI systems.
Understanding of prompting strategies, context windows, and model behavior optimization.
Experience with real-time decisioning systems or workflow orchestration engines.