Senior Data Engineer - AI

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market. What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture. Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform. Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins – big and small. Supported by operating principles of being strategy-led, values -based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together! We're seeking a Senior Data Engineer to work across the full stack of Anaplan AI applications. You will build transformative AI capabilities from the ground up, from model integration and prompt engineering to contributing to the technical direction for how we ingest, transform, store, serve, and govern the data that powers our LLM-based and agentic systems. You will build user-facing AI features that can be used in real-time, directly impacting how businesses plan and make decisions. This role requires both a strong foundation in machine learning and excellent data engineering skills, offering a unique opportunity to grow at the intersection of AI and enterprise software. Your Impact Contribute to the data architecture, design, and deployment of scalable Generative AI and Machine Learning systems into production environments. Develop end-to-end GenAI features, including backend API services, model integration, model monitoring, evaluations, and deployments. Integrate and optimise LLMs for specific use cases in business planning, including prompt engineering and RAG implementation. Design and build the retrieval and knowledge layer powering our RAG and agentic workloads, such as vector databases, graph databases, knowledge graphs, hybrid search, and embedding pipelines. Help design the knowledge graph that captures the semantics of customer models, metrics, hierarchies, and relationships. Build the data plane for evaluation and continuous improvement, working with cutting-edge conversational and agentic AI technologies. Engineer the feature and context pipelines that feed forecasting and anomaly-detection models at customer scale, balancing batch and streaming patterns. Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics. Your Skills Extensive data engineering experience with a track record of delivering complex projects. Hands-on experience building and shipping AI/ML products in production. Practical experience with LLM-based systems: RAG architectures, embedding pipelines, prompt and response logging, and evaluation frameworks. Hands-on expertise with vector databases, graph databases, and knowledge graphs. End-to-end exposure to the model development lifecycle, including experience training and deploying ML models in production environments. Solid knowledge of LLM APIs, prompt engineering, and conversational AI patterns. Strong expertise in MLOps and LLMOps, ensuring scalable, reliable, and monitorable model deployments. Proficiency in Python and modern software development practices (testing, code review, CI/CD). Desirable Hands-on experience with cloud-native ML infrastructure platforms. Knowledge of vector databases (e.g., Pinecone, Weaviate, Qdrant) and embedding models. Experience with model serving frameworks (e.g., vLLM, TensorRT, Ray). Background in forecasting, planning, or analytics applications.