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

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