• Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training, and deployment systems. • Troubleshoot issues in the entire machine learning infrastructure, from Linux, Docker, and Kubernetes up to the highest levels of our ML stack. Resolve issues, improve system performance, and make our stack the best in the industry. • Assist in the design and development of on-premises MLOps solutions to support the delivery of machine learning models, and a seamless handover between research and productionization of ML artifacts. • Drive and uphold high engineering standards, bringing consistency to codebases encountered and ensuring software is adequately reviewed, tested, and integrated. • Optimize existing models for better performance and throughput. • Incorporate ML model training, validation, and evaluation settings in addition to traditional coding tests like unit and integration testing. • Build and maintain tools for deployment, monitoring, and operations. - Continuously refine and enhance CI/CD workflows to support the evolving needs of the machine learning infrastructure.

• Responsible for transforming AI capabilities into real business solutions by building systems that not only respond but also execute tasks, automate processes, and deliver results. • Will work at the intersection of software engineering, data, and AI, designing and operating intelligent applications integrated into the corporate environment.

• Developing, training, tuning and running ML models on our data to get answers to key questions. • Integrating and maintaining these models within our software products. • Monitoring model inference outputs to assess performance degradation over time. • Establishing feedback mechanisms with end users to continuously improve model performance. • Developing and integrating new analytical algorithms to run against large datasets. • Guiding our teams on how best to utilise AI/ML in ethical, appropriate and values driven ways.

• Building AI-powered internal tools and data-driven systems used across the organization - analytics platforms, reporting automation, workflow intelligence. • Developing AI/ML-driven platforms that process and analyze large-scale operational data from 30M+ sites across our product suite. • Designing and deploying RAG systems, LLM-powered features, and agentic workflows that generate actionable insights for internal teams. • Building data pipelines that ingest, transform, and serve behavioral and operational data at scale. • Prototyping and shipping AI solutions that streamline internal processes, reduce manual effort, and improve decision-making. • Evaluating and selecting the right models, frameworks, and infrastructure for each use case - balancing performance, cost, and latency. • Writing clean, well-tested, production-grade Python code with proper error handling, logging, and observability. • Collaborating with product teams and stakeholders to understand what they need, then building tools they actually use. • Keeping up with the rapidly evolving AI landscape and bringing new ideas, tools, and approaches to the team. • Communicating with the team and supporting your peers using chat, audio, and video.