Principal Engineer (A.I.)

Requirements

• 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 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) Advanced degree (Master's or Ph.D.) in Computer Science, Artificial Intelligence, Machine Learning, or a strongly related quantitative field.
• (Desirable) Hands-on experience with cloud-native ML infrastructure platforms.
• (Desirable) Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models.
• (Desirable) Experience with model serving frameworks (vLLM, TensorRT, Ray).
• (Desirable) Experience with A/B testing and experimentation frameworks for AI features.
• (Desirable) Contributions to open-source ML projects or research publications.
• (Desirable) Experience with model observability tools (LangSmith, W&B, MLflow).

What the job involves

• 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 optimise LLMs for specific use cases in business planning, including prompt engineering, 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.
• Optimise 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 behaviour.
• Collaborate with data scientists to productionise ML models and forecasting algorithms.