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).
Hands-on experience with cloud-native ML infrastructure platforms.
Knowledge of vector databases (e.g., Pinecone, Weaviate, Qdrant) and embedding models.
Experience with model serving frameworks (e.g., vLLM, TensorRT, Ray).
Background in forecasting, planning, or analytics applications.
Experience with A/B testing and experimentation frameworks for AI features.
Experience with model observability tools (e.g., LangSmith, W&B, MLflow).

Senior Data Engineer - AI

Summary