Senior Data Engineer - AI

Senior Data Engineer

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, including model integration and prompt engineering, and contribute 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 real‑time, user‑facing AI features that directly shape business planning and decision‑making. This role demands strong machine learning expertise paired with data engineering skills—providing unique growth at the intersection of AI and enterprise software.

• 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 optimize LLMs for specific business planning use cases, 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.

• 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).
• 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, Tensor
  
  RT, Ray).
• Background in forecasting, planning, or analytics applications.
• Experience with A/B testing and experimentation frameworks for AI features.
• Experience with model observability tools (e.g., Lang Smith, W&B, MLflow).