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Nem Mehta
Intern AI & Machine Learning Engineer specializing in computer vision and edge deployment
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About
Built and shipped a real-time AI robotic inspection system, using a synthetic data generation pipeline to address rare edge cases—cutting data collection costs ~60% and boosting hard-scenario accuracy ~20%. Experienced in productionizing ML on constrained Jetson hardware and orchestrating end-to-end ML workflows with Airflow/Docker/Kubernetes, with a metrics-driven approach to reliability, evaluation, and stakeholder communication.
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Key Strengths
- Built and deployed real-time robotic inspection AI to production
- Reduced data collection costs ~60% via synthetic data pipeline
- Improved difficult-scenario detection accuracy ~20% using real+synthetic training
- Strong domain-gap mitigation: controlled real/synthetic ratios, domain randomization, real holdout validation
- Edge-device deployment optimization on Jetson under tight latency/memory constraints
- End-to-end ML pipeline orchestration with Airflow (ingestion through retraining) with robust retries/logging
- Designs reliable agent/workflow systems with measurable metrics, guardrails, and scenario-based testing
- Pragmatic model/RAG/prompt selection driven by constraints (latency, budget, privacy) and evaluation
- Effective collaboration with non-technical stakeholders using measurable goals and visual, real-footage validation
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