Principal AI Engineer

Role summary

Responsibilities

• Lead the architecture, design, and deployment of scalable generative AI and machine learning systems.
• Develop end-to-end generative AI features covering backend services, model integration, evaluations, monitoring, and deployment.
• Integrate and optimize LLMs for business planning use cases, including prompt engineering and retrieval-augmented generation.
• Build conversational interfaces and agentic workflows for natural-language planning tasks.
• Implement evaluation frameworks for accuracy, latency, and user satisfaction.
• Design APIs for Anaplan’s platform and third-party integrations.
• Optimize model inference pipelines for performance, cost, and scalability.
• Implement monitoring, logging, and observability for generative AI systems.
• Collaborate with data scientists to productionize ML models and forecasting algorithms.

Required skills and experience

• Have extensive hands-on professional experience in AI, machine learning, or related engineering domains.
• Have extensive experience training and deploying machine learning models in production.
• Have deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns.
• Have experience fine-tuning LLMs for enterprise applications.
• Have strong MLOps and LLMOps expertise.
• Have experience with agentic frameworks and autonomous agent architectures.
• Be proficient in Python and modern software development practices, including testing, code review, and CI/CD.
• Have delivered complex technical projects on time and to a high standard.

Desirable qualifications

• An advanced degree in computer science, AI, machine learning, or a related quantitative field is desirable.
• Experience with cloud-native ML infrastructure, vector databases, embedding models, model serving, experimentation, or observability tools is desirable.
• Contributions to open-source ML projects or research publications are desirable.

Workplace

• The provided job description does not specify a remote, hybrid, onsite, or field-work arrangement.