Applied AI Engineer at Maisa AI

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Full Time

Start Date

Immediate

Expiry Date

19 Sep, 26

Salary

0.0

Posted On

21 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Agent Workflows, LLMs, RAG Systems, Python, Prompt Optimization, Context Engineering, ML System Evaluation, Software Engineering, Performance Optimization, Production Deployment

Industry

Software Development

Description

Applied AI Engineer Location: Fully Remote Team: Research Welcome to Maisa - Making AI Accountable! 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. About the role… * 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. What you’ll do… * 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. What you’ll bring… * 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. Why join Maisa! You will be joining one of Europe’s most exciting early-stage AI start-ups, where you’ll have the opportunity to work with cutting-edge Agentic Process Automation that’s reshaping how enterprises approach AI deployment. You will get to directly influence how major multinational organizations transform critical business processes, working on genuinely differentiated technology that solves real enterprise AI challenges. Following our recent $25m Seed Round [https://techcrunch.com/2025/08/27/maisa-ai-gets-25m-to-fix-enterprise-ais-95-failure-rate/], backed by leading Venture Capital firms including Creandum, Forgepoint, NFX, and Village Global, we’re scaling quickly and realising significant enterprise traction. This is your opportunity to help solve real AI enterprise challenges, working alongside deep technical and industry experts, where you will be challenged daily and expedite your learning and development. Maisa is committed to Equal Employment Opportunity through attracting and retaining a complementary team of employees and building an inclusive environment for all.

Responsibilities

Design and implement production-ready AI agent workflows using KPU technology to solve enterprise problems. Build evaluation frameworks to measure AI performance and optimize workflows for reliability and explainability.