AI Researcher

Application instructions: Interested applicants, please apply for this job at this link: https://grnh.se/8pgf95h62us Responsibilities We are looking for an exceptional AI Researcher to join our growing AI team. In this role, you will design, build, deploy, and improve ML/LLM-powered services and features that power intelligent automation and AI-driven product experiences across the Workato platform. You will work closely with our Engineering, Product, and Design teams to define and track product metrics and evaluation strategies, design customer-facing experiments and dive deep to provide actionable insights.This role is ideal for someone who combines strong ML/LLM intuition, software engineering skills and a practical mindset for shipping reliable, scalable AI systems. Build and improve AI services using LLMs and custom machine learning models for production use cases. Design, develop, and operate ML/LLM systems end-to-end, from prototyping to deployment and monitoring. Write high-quality Python code that is testable, maintainable, and efficient. Improve validation, observability, and performance monitoring for ML services (quality, latency, reliability, cost). Partner cross-functionally with product managers, platform engineers, and other stakeholders to ship AI-powered product capabilities. Evaluate and improve existing implementations by identifying bottlenecks, bugs, and opportunities for optimization. Design controlled experiments to test the features for our AI-based products and perform deep analysis from the results to find actionable insights Contribute to technical design and code reviews , helping raise engineering quality across the team. Experiment and iterate on model behavior, prompting, retrieval, tool use, or orchestration strategies to improve user outcomes. Requirements Qualifications / Experience / Technical Skills Experience with tool-use agents or workflow-aware AI systems. Experience building AI products in enterprise SaaS environments. Experience with A/B testing and statistical significance techniques. Experience with LLMOps/MLOps tooling and practices (monitoring, evaluation pipelines, model rollout, CI/CD). Experience working with modern data warehouses such as Amazon Redshift Snowflake.