Senior Data Engineer – AI

Overview

In this role you will architect and deploy GenAI/ML capabilities across Anaplan’s AI applications, shaping how data powers enterprise decision making. You’ll work on model integration, prompt engineering, and data governance for real-time features used by customers. You’ll influence the end-to-end data stack—from ingestion to serving—within a fast-growing AI-driven platform. You’ll collaborate with cross-functional teams to deliver impactful, scalable AI features that customers rely on for planning and forecasting. This is a hands-on position with a clear impact on product direction and performance at scale.

Responsibilities

• Contribute to data architecture, design, and deployment of scalable Generative AI/ML systems into production
• Develop end-to-end GenAI features, including backend APIs, model integration, monitoring, evaluations, and deployments
• Integrate and optimize LLMs for business planning use cases, including prompt engineering and RAG implementation
• Design and build retrieval and knowledge layers (vector/graph databases, knowledge graphs, hybrid search, embedding pipelines)
• Assist in designing the knowledge graph capturing semantics of customer models, metrics, hierarchies, and relationships
• Build the data plane for evaluation and continuous improvement with conversational/agentic AI technologies
• Engineer feature and context pipelines for forecasting and anomaly detection at customer scale (batch and streaming)
• Implement evaluation frameworks to measure GenAI feature quality (accuracy, latency, user satisfaction)

Key requirements

• 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, embeddings, prompt/response logging, evaluation frameworks
• Hands-on expertise with vector databases, graph databases, and knowledge graphs
• End-to-end exposure to the model development lifecycle, including training and deploying ML models in production
• Solid knowledge of LLM APIs, prompt engineering, and conversational AI patterns
• Strong expertise in MLOps and LLMOps for scalable, reliable deployments
• Proficiency in Python and modern software development practices (testing, code review, CI/CD)

• LLM-based systems (RAG, embeddings, prompt engineering)
• Vector databases, graph databases, knowledge graphs
• MLOps and LLMOps
• Python, CI/CD, testing
• Model development lifecycle (training, deployment, monitoring)

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