Design and deploy end-to-end AI-powered automation workflows that eliminate manual, repetitive tasks across sales, HR, operations, and commercial functions — using tools such as n8n, Azure Logic Apps, or equivalent.
Stay at the forefront of industry trends regarding AI algorithms, LLM technologies, and open-source frameworks. Conduct technical research, proof-of-concept, and innovative implementations while contributing to technical documentation, solution frameworks, and internal toolkits to support team-wide technology advancement.
Mathematical Innovation for novel AI techniques - we welcome original and first-principles thinking. You will be encouraged to ideate out-of-box fundamental scientific solutions and given bandwidth to experiment.
Gather business requirements through various methods such as interviews, document analysis, workshops, and workflow analysis, articulating these requirements in terms of target user roles and objectives. Translate business and user needs into clear technical requirements, solution designs and measurable success criteria for AI applications.
Support business functions, which use our no-code agent platform (Copilot Studio), by providing expertise and best practices in AI agents and their practical applications.
Collaborate with solution architects, AI engineers, and software engineering teams to translate business requirements into secure, scalable, production-ready solutions.
Knowledge Base Development: Design and implement structured knowledge bases that enhance LLM output quality by integrating domain-specific data, ontologies, and automated document indexing.
Kubernetes Service PlatformDevelopment of managed Kubernetes services with GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, resource isolation, and support for large-scale AI training and inference workloads
Focus on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data (marketing assets, brand collateral, campaign performance data, client media libraries) to deploy AI or ML solutions from theoretical data science models into production services.
Uphold rigorous standards across data preparation, modelling, versioning, deployment, and post-deployment monitoring to ensure solutions remain fit for purpose, and continually explore new techniques to improve performance