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.
• Minimum 7 years of experience in AI/ML engineering with leadership exposure • Deep expertise in Generative AI, LLM ecosystems, and prompt engineering • Strong hands-on experience designing ReAct agents and agentic AI workflows • Advanced experience designing and optimizing RAG architectures • Strong understanding of advanced prompting techniques (few-shot, chain-of-thought, self-consistency, tool calling, structured outputs) • Experience with LangGraph, LangChain, Azure OpenAI, and OpenAI APIs • Experience implementing Model Context Protocol (MCP) or structured AI interaction frameworks • Strong proficiency in Python for AI system and backend development • Strong understanding of asynchronous programming and distributed system design • Experience designing microservices-based AI platforms • Experience collaborating closely with DevOps teams for containerized deployments (Docker), CI/CD, and cloud infrastructure • AI system monitoring and observability experience using Langfuse, logging frameworks, telemetry systems, and evaluation pipelines • Strong SQL and data handling expertise Additional Technical Expertise • Strong foundation in traditional machine learning techniques (classification, regression, clustering, forecasting) • Hands-on experience with ML frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost • Experience in feature engineering, model evaluation, and hyperparameter optimization • Experience working with vector databases, embeddings, and semantic search systems • Exposure to MLOps practices, model deployment strategies, and CI/CD pipelines • Knowledge of AI security, governance, and ethical AI implementation • Experience working in Azure-based AI and ML environments (Azure ML, Azure AI Services preferred) Leadership & Engagement • Proven experience leading AI engineering teams and defining technical vision • Strong stakeholder, client, and vendor management capabilities • Ability to translate complex business challenges into scalable AI architectures • Experience collaborating across global teams and time zones • Strong communication, ownership mindset, and architectural decision-making ability
Our Benefits Flexible working environment Volunteer time off LinkedIn Learning Employee-Assistance-Program (EAP) NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ reg