• 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

• 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

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

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