AI Lead Developer / Architect at NielsenIQ

We’re looking for an AI Lead Developer with deep expertise in Generative AI and Agentic AI systems, combined with strong architectural leadership and client engagement capabilities. This is a high-visibility role where you’ll architect scalable GenAI platforms, lead engineering teams, manage enterprise AI engagements, and build production-grade intelligent systems powered by LLMs, RAG architectures, ReAct agents, and advanced AI workflows. What You’ll Do • Architect and lead development of enterprise-grade Generative AI solutions using LLMs and advanced prompting strategies. • Design and implement Agentic AI workflows, including ReAct (Reasoning + Acting) agents and multi-agent orchestration systems using frameworks such as LangGraph and LangChain. • Build scalable Retrieval-Augmented Generation (RAG) architectures with context engineering, evaluation pipelines, and guardrails. • Develop and optimize advanced prompting techniques (few-shot, zero-shot, chain-of-thought, self-reflection, structured prompting, tool-augmented prompting). • Implement structured interaction frameworks using Model Context Protocol (MCP) or similar standards. • Develop asynchronous, distributed AI applications optimized for performance and horizontal scale. • Design modular AI systems using microservices architecture and API-first design principles. • Implement AI monitoring, observability, and evaluation frameworks using tools such as Langfuse (for tracing, performance monitoring, hallucination tracking, and cost analysis). • Work closely with DevOps teams to productionize AI solutions using Docker, containerization best practices, CI/CD pipelines, and cloud-native deployment models. • Lead client and vendor engagements as the AI technical authority — driving architecture decisions and AI solution roadmaps. • Mentor and manage AI/ML engineering teams, setting technical standards and architectural best practices. • Deploy and manage AI systems in Azure cloud environments (preferred). • Develop backend AI services, APIs, and data pipelines using Python and SQL. • Drive responsible AI practices, governance frameworks, and model lifecycle management.