Key skills
PythonLLM orchestrationAPI integrationcloud platformsdata science fundamentalsdocument processingcontinuous improvementresearchinnovationethicscompliancecollaborationAIMLNLPLLMOpenAILangChainMLOpsAnalyticsFastAPIAWSAzureGCPOpenTelemetryRESTfulCollaboration
About this role
Dentsu is a global company seeking a Lead AI Engineer to design, build, and deploy AI capabilities that enhance their products. The role involves collaborating with cross-functional teams to develop scalable AI systems for various applications such as content personalization and audience analytics.
Responsibilities:
- AI Solution Development: Architect, develop, and maintain AI systems for media products
- Collaboration : Partner with product, engineering, and data teams to translate business needs into technical solutions
- Deployment & Scaling : Implement AI models in production environments, ensuring reliability, scalability, and performance across global markets
- Continuous Improvement : Monitor, evaluate, and iterate on AI models using real-world data and feedback
- Research & Innovation : Stay current with AI/ML advancements; prototype and recommend new technologies to keep our products at the forefront of innovation
- Ethics & Compliance : Champion responsible AI practices, ensuring fairness, transparency, and compliance with data privacy regulations
Requirements:
- Master's degree in Computer Science, Engineering, or related field
- Strong Python skills, with experience in LLM orchestration libraries (e.g., LangChain, LlamaIndex, Semantic Kernel), chain-of-thought, and structured reasoning approaches
- Demonstrated experience with enterprise LLMs – their APIs, performance challenges, and how to mitigate those (OpenAI, Anthropic, Cohere, etc.)
- Demonstrated production experience with API conventions and integration into pipelines, e.g. RESTful API, FastAPI, or similar
- Demonstrated experience integrating multiple approaches for extracting highest-quality output from LLMs, e.g. prompt engineering, multi-agents, structured output validation, hybrid RAG, in-context learning (ICL), or NLP- or ontology-powered layering
- Proficiency with document processing pipelines (text extraction, chunking, embedding)
- Demonstrated production experience with measuring and improving AI systems, tracing and AI observability (e.g. Arize, OpenTelemetry, LangSmith)
- Strong foundation in data science fundamentals, including data analysis, statistical methods, and experience working with structured and unstructured data
- Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools
- PhD in Computer Science, Engineering, or related field