Principal Engineer, Anaplan

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

Desirable

• We're seeking a Principal Engineer, AI who can work across the full stack of Anaplan AI applications, from model integration and prompt engineering to building intuitive user interfaces
• You'll build production-ready AI features that empower business users to leverage the power of GenAI within their planning workflows, requiring both deep ML knowledge and strong software engineering skills
• Lead the 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
• Build conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language
• Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics
• Design and develop APIs that expose AI capabilities to Anaplan's platform and third-party integrations
• Optimize model inference pipelines for performance, cost, and scalability in production environments
• Implement monitoring, logging, and observability for GenAI systems to track usage, errors, and model behavior
• Collaborate with data scientists to productionise ML models and forecasting algorithms