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Eric Redondo

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Palo Alto, California, United States

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Résumé


Jobs verified_user 0% verified
  • Snorkel AI
    Senior AI Engineer / MLOps Architect
    Snorkel AI
    Sep 2022 - Mar 2026 (3 years 7 months)
    Working on enterprise-scale LLM and weak supervision systems for production AI deployments. • Designed and deployed production RAG systems for enterprise knowledge search serving 50K+ internal users, supporting low-latency (<1.2s p95) responses. • Built programmatic data labeling pipelines using Snorkel Flow and weak supervision techniques, reducing manual labeling costs by 60% while improving dataset coverage. • Architected LLM evaluation framework including Prompt regression testing integrated into CI, LLM-as-judge scoring, Retrieval confidence scoring for hallucination mitigation, Human-in-the- loop validation workflows • Implemented embedding drift detection using cosine similarity distribution shift and statistical PSI metrics. • Reduc
  • K
    Senior Machine Learning Engineer
    Kohl's Careers
    Nov 2020 - Aug 2022 (1 year 10 months)
    Focused on production ML architecture and scalable AI systems for enterprise customers migrating to cloud-native ML environments. • Designed and deployed production ML systems using SageMaker, EKS, and Lambda supporting high-availability inference workloads. • Built automated training and deployment pipelines using SageMaker Pipelines and Step Functions, reducing release friction across multiple teams. • Implemented multi-model endpoints serving heterogeneous models with cost-aware routing strategies. • Reduced training infrastructure costs by 28% using spot training, distributed training strategies, and dynamic scaling. • Designed monitoring systems using CloudWatch, Prometheus, and custom drift detection pipelines (KL divergence, data dis
  • Linedata
    Data Engineer / Machine Learning Engineer
    Linedata
    Sep 2019 - Oct 2020 (1 year 2 months)
    Worked within large-scale data infrastructure teams supporting analytics and ML initiatives across high-traffic consumer platforms. • Designed distributed ETL pipelines processing petabyte-scale datasets using Spark and internal data platforms. • Built real-time streaming pipelines using Kafka and Kinesis for near real-time analytics. • Partnered with applied scientists to productionize recommendation and forecasting models. • Reduced query latency by 35% through storage optimization and indexing strategies. • Supported experimentation infrastructure for A/B testing and model performance evaluation. • Contributed to cross-team data governance and reliability standards.
Education verified_user 0% verified
  • Stanford University
    MS, Computer Science
    Stanford University
    Apr 2015 - Aug 2019 (4 years 5 months)
    Specialization: Artificial Intelligence
  • Texas A&M University
    BS, Computer Science
    Texas A&M University
    Aug 2012 - Mar 2015 (2 years 8 months)
    Minors in Mathematics and Mechanical Engineering