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Srijan Dokania
Junior Robotics & Machine Learning Engineer specializing in perception, SLAM, and edge AI
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Built and deployed an Azure-based, fine-tuned CLIP visual retrieval system at Staples for a ~300k-item product catalog, improving edge-case recall by 12% by engineering a custom delta-similarity/dynamic-margin loss. Also has robotics experience using ROS2 for sensor/compute orchestration, including GPS-time-synchronized sensor triggering for robot swarms and latency-bounded optical-flow benchmarking for edge deployment.
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Key Strengths
- Shipped production visual search/retrieval system for ~300k-item catalog
- Improved edge-case recall by 12% via custom delta-similarity/dynamic-margin contrastive loss
- Strong understanding of hard negatives and training stability techniques (warmup + gradual loss weighting + low LR)
- Orchestrated end-to-end ML pipelines from ingestion to deployment using cloud tooling (Azure/AWS/PySpark)
- Resolved PySpark reliability issues from data skew using aggressive checkpointing
- Robotics orchestration expertise with ROS2 across sensors/compute/odometry
- Built GPS-time-synchronized distributed sensor triggering to mitigate network-latency sync issues in robot swarms
- Reliability-focused AI workflow design using modular/hybrid retrieval (keyword + semantic) to reduce errors
- Performance/latency-driven model evaluation (e.g., optical flow <90ms; benchmarking on KITTI) before edge deployment
- Effective collaboration with non-technical stakeholders (merchandisers) by translating qualitative feedback into measurable model improvements
Reference Highlights
- mature communicator
- explains complex concepts in simple, layman terms
- strong stakeholder management and communication
- able to translate data insights into clear visual explanations
- good at designing and shipping AI/LLM products
- effective at handling technical questions during workshops/demos
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Experience
Education
Awards
Skills
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Publications
4 publications
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