Senior Data Engineer – AI
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Job Description
Senior Data Engineer at Anaplan building AI capabilities and solutions for business decision-making. Working across the full stack of Anaplan AI applications and developing innovative features.
Responsibilities:
- Build transformative AI capabilities from the ground up, including model integration and prompt engineering.
- Contribute to the technical direction for how we ingest, transform, store, serve, and govern the data that powers our LLM-based and agentic systems.
- Build real-time, user-facing AI features that directly shape business planning and decision-making.
- Contribute to the data 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.
- Design and build the retrieval and knowledge layer powering our RAG and agentic workloads, such as vector databases, graph databases, knowledge graphs, hybrid search, and embedding pipelines.
- Help design the knowledge graph that captures the semantics of customer models, metrics, hierarchies, and relationships.
- Build the data plane for evaluation and continuous improvement, working with cutting-edge conversational and agentic AI technologies.
- Engineer the feature and context pipelines that feed forecasting and anomaly-detection models at customer scale, balancing batch and streaming patterns.
- Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics.
Requirements:
- Extensive data engineering experience with a track record of delivering complex projects.
- Hands-on experience building and shipping AI/ML products in production.
- Practical experience with LLM-based systems: RAG architectures, embedding pipelines, prompt and response logging, and evaluation frameworks.
- Hands-on expertise with vector databases, graph databases, and knowledge graphs.
- End-to-end exposure to the model development lifecycle, including experience training and deploying ML models in production environments.
- Solid knowledge of LLM APIs, prompt engineering, and conversational AI patterns.
- Strong expertise in MLOps and LLMOps, ensuring scalable, reliable, and monitorable model deployments.
- Proficiency in Python and modern software development practices (testing, code review, CI/CD).
Benefits:
- Health insurance
- Flexible working arrangements
- Professional development opportunities
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