Front-End & Back-End Integration: Participate in web application development, seamlessly integrating AI capabilities (API / LLM services) into business systems to deliver intuitive user experiences.
Dealer & Sales Alignment: Formulate robust training frameworks supporting Sales, Aftersales, and the authorized dealer network to elevate brand service standards and customer satisfaction.
Lead the implementation and operationalisation of knowledge base and retrieval-augmented generation solution stacks to support scalable GenAI and agentic use cases across business domains
Collaborate closely with Product, ML/Algorithm, and Design teams to align prompts with product feature logic, ensuring quality and stability across critical user journeys.
The role provides technical authority across AI platform strategy, foundation models, agentic AI, model lifecycle management, AI engineering frameworks and platform capabilities. It works closely with Enterprise Architecture, AI Platform Engineering, AI Platform Operations, Cybersecurity, AI Solution Architects and business stakeholders. Success means continuously evolving an interoperable, reliable and enterprise-ready AI platform while providing technical direction to architects and engineers.
Work within established AI-native engineering workflows, collaborating with AI agents across implementation, testing, debugging, and documentation while validating every output.
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
Engineering leadership and delivery: Lead a multidisciplinary team of full-stack developers and project managers, including offshore resources, to design, build, operate, and continuously improve AI-powered, web-based, and customer-facing digital applications.
Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
Connect business and technology. Partner with technical trainers on experiment design and failure diagnosis. In the technology variant, additionally build Python evaluation scripts, manage dataset versions and support approved fine-tuning experiments where justified.