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Christopher Sipola
Senior Machine Learning Engineer specializing in LLMs and recommendation systems
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About
ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.
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Key Strengths
- owned Spotify AI DJ personalization and LLM orchestration end-to-end
- shipped production GenAI feature with double-digit engagement and listening-depth gains
- deep expertise in real-time feedback loops using skips, replays, and session signals
- strong RAG architecture judgment over fine-tuning for grounded personalization
- balanced latency, cost, quality, and safety in production LLM systems
- built scalable ML/LLM infrastructure on GCP, Kubeflow, MLflow, Airflow, Docker, and GKE
- designed robust safety systems with grounding, filters, confidence checks, and human review
- improved recommendation quality by blending short-term behavior with long-term taste embeddings
- created shared Python service layer that increased team experiment velocity and reliability
- strong production monitoring mindset across latency, drift, diversity, skip rate, and session depth
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