Principal Engineer – AI

Overview

As Principal Engineer, AI, you will lead end-to-end GenAI features across the full AI stack for Anaplan’s planning platform. You will design, deploy and monitor scalable AI and ML systems in production, partnering with data scientists and platform engineers to deliver enterprise-ready capabilities. You’ll optimize LLMs, implement prompt engineering, and build user-friendly interfaces that enable business users to leverage GenAI in planning workflows. This role combines deep ML expertise with strong software engineering to shape AI-powered planning at scale, within a collaborative, innovation-driven culture.

Responsibilities

• Lead architecture, design and deployment of scalable Generative AI and ML systems into production
• Develop end-to-end GenAI features including backend APIs, model integration, monitoring, evaluation and deployment
• Integrate and optimize LLMs for enterprise planning use cases (prompt engineering, RAG)
• Build conversational interfaces and agentic workflows for natural-language planning tasks
• Implement evaluation frameworks to measure GenAI feature quality (accuracy, latency, user satisfaction)
• Design and develop APIs exposing AI capabilities to Anaplan’s platform and third-party integrations
• Optimize model inference pipelines for performance, cost, and scalability in production
• Implement monitoring, logging and observability for GenAI systems to track usage and behavior
• Collaborate with data scientists to productionise ML models and forecasting algorithms

Key requirements

• Extensive hands-on experience in AI/ML or related engineering domains
• End-to-end model lifecycle experience including training and deploying ML models in production
• Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns
• Experience fine-tuning LLMs for enterprise applications
• Strong expertise in MLOps / LLMOps for scalable, reliable deployments
• Experience with agentic frameworks and autonomous agent architectures
• Proficiency in Python and modern software dev practices (testing, code review, CI/CD)
• Proven track record delivering complex technical projects on time with high quality

• strong collaboration across cross-functional teams
• clear communication of complex technical concepts
• problem-solving and proactive ownership
• LLM APIs
• prompt engineering
• RAG

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