CV

Leading development of Nike’s AI/ML and GenAI platforms with a focus on scalable training, inference, and monitoring infrastructure.

• Built unified debugging and observability tools that reduced model pipeline failures by 70%.
• Led fine-tuning and serving of large vision-language models including SAM2, GroundingDino, and CLIP.
• Created Terraform-based templates that cut GenAI deployment time by 50%.
• Authored best-practice playbooks adopted across the Nike AI Community of Practice.

Drove engineering excellence and production reliability for Nike’s AI ecosystem.

• Migrated legacy ML workloads to AWS SageMaker, enabling cross-team scalability.
• Optimized PySpark pipelines (-70 % runtime, +30 % accuracy).
• Developed standardized AWS/logging SDKs reducing production incidents by 90 %.
• Established CI/CD standards, canary testing, and monitoring for GenAI services.

Delivered applied ML solutions and research prototypes in vision, NLP, and AR.

• Deep Learning Photo Aesthetics – created TensorFlow-based classifier using AVA dataset.
• Heineken AR Cheers Campaign – built object-detection model powering adaptive AR marketing.
• Climate Change Sentiment Analysis – used BERT and ensemble methods to track public opinion over time.

• Aggregated multi-source flight data for advanced analytics.
• Built ML-driven tools improving Guidance Navigation & Control algorithm accuracy.

• Implemented signal-processing and control algorithms for radar sensor systems.

• Enhanced telecom signaling performance for high-volume markets.

• Developed embedded C drivers and automated test frameworks for GNC systems.

• Built internal social platform and IoT prototypes on Raspberry Pi and Google Glass.