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Our agentic process automation platform helps enterprises automate complex, decision-heavy processes that traditional automation can’t handle and GenAI can’t be trusted with. We enable organizations to scale operations, resist hallucinations, and bring end-to-end visibility and control to your most complex processes. Powered by a new kind of computing platform, Maisa combines AI-driven problem solving with programmatic execution, so every action is reliable, auditable, and built for enterprise scale. You'll join our AI R&D team building production-ready AI agent systems on top of our KPU technology. This is a hands-on builder role focused on implementing, evaluating, optimizing and deploying agentic workflows that solve real enterprise problems. You'll bridge research and production, transforming cutting-edge AI capabilities into reliable, accountable systems that meet enterprise requirements.
Job Responsibility:
Design and implement end-to-end AI agent workflows using our KPU technology, working directly with customers to rapidly iterate on solutions
Build robust evaluation datasets and frameworks to measure agents/AI performance, reliability, and accuracy
Optimize workflows for performance, explainability, and enterprise-grade reliability
Push the boundaries of what's possible with LLMs, RAG systems, and AI agents in production environments
Stay at the forefront of Agentic AI development, continuously applying new techniques to improve our platform
Requirements:
Demonstrable experience building and shipping AI-powered features to production environments
Strong hands-on experience with LLMs, evaluations, prompt optimization and context engineering in real-world applications
Strong background as SWE with proficiency in Python for production system development and code quality frameworks/practices
Understanding of ML system evaluation methodologies and performance optimization
Ability to translate complex AI capabilities into practical enterprise applications
Self-starter mentality who thrives as an individual contributor in fast-paced, ambiguous startup environments
Passion for building AI systems that are reliable, transparent, and trustworthy