Senior Data Engineer – AI

Anaplan

Senior Data Engineer at Anaplan building AI capabilities and solutions for business decision-making. Working across the full stack of Anaplan AI applications and developing innovative features.

Posted 6/6/2026full-timeRemote • Pennsylvania • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build transformative AI capabilities from the ground up, including model integration and prompt engineering.
Contribute to the technical direction for how we ingest, transform, store, serve, and govern the data that powers our LLM-based and agentic systems.
Build real-time, user-facing AI features that directly shape business planning and decision-making.
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.

Requirements

What you’ll need

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).

Benefits

Comp & perks

Health insurance
Flexible working arrangements
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

AI capabilitiesmodel integrationprompt engineeringGenerative AIMachine Learningbackend API servicesmodel monitoringevaluation frameworksMLOpsLLMOps

Soft Skills

communicationcollaborationproblem-solvingproject management