Senior AI Engineer

MeshDefend is an early-stage VC funded startup that is building AI-Native Agentic Operating System for Enterprise Data Infrastructure. We are creating AI Agents to make Data Infrastructure Intelligent, Autonomous and Secure. Our founding team is composed of seasoned technologists and inventors, each with a track recordof building global product portfolios valued in the billions. Here, you won't just follow ablueprint; you'll take the wheel and help set the course, leveraging cutting-edge solutions to redefine what's possible. If you're driven by innovation and crave hands-on influence over both product direction and technical architecture, this is the place to make your mark.

We are seeking an AI Engineer who will shape the technical direction of our AI platform and engineer its core intelligence. This role is for someone who enjoys building autonomous, learning systems end to end, from experimentation and prototyping through production deployment at scale. We are specifically looking for candidates who have taken AI agents or LLM-based systems into production and have firsthand experience handling reliability, monitoring, failure modes, and continuous iteration.

You will work closely with Founders, product design, and engineering to build AI systems that reason, plan, and act autonomously. Beyond building models and agents, you will be responsible for taking agentic systems into real production environments, ensuring reliability, security, observability, and continuous improvement.

This is a hands-on role with real ownership. You will help define how AI is built, deployed, and operated across the company, and will play a key role in establishing strong AI and AI operations foundations.

Key Responsibilities

• Design, build, and iterate on autonomous AI agents
• Improve current agentic systems and build new ones from prototype to production
• Architect agentic workflows involving planning, tool usage, memory, and feedback loops
• Select, train, fine-tune, and evaluate models for real-world constraints such as latency, cost, and robustness
• Implement AI systems using modern agent frameworks and orchestration tools
• Partner with Data Engineers to define, curate, and maintain high-quality training and evaluation datasets