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Hari Shankar Raghuraman
Mid-level AI/ML Engineer specializing in scalable ML, NLP, and MLOps
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About
ML/AI engineer with strong production depth across classical ML, MLOps, LLM/RAG, and scalable Python data platforms, with experience at Cisco and Accenture. Stands out for tying technical decisions to measurable business outcomes, including $1.2M annual savings, 40% faster support resolution, and broad internal adoption of shared engineering frameworks.
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Key Strengths
- End-to-end ownership of production ML systems
- Strong MLOps and deployment architecture
- Business-focused model tradeoff decisions
- Delivered 22% reduction in false positive escalations
- Saved approximately $1.2M annually in support costs
- Reduced model degradation detection time from 3 weeks to 4 days
- Cut deployment time by 45% through pipeline automation
- Shipped RAG support assistant with 40% reduction in mean resolution time
- Practical LLM evaluation using retrieval metrics plus expert review
- Productionizing research models with latency/cost/safety tradeoffs
- Improved reliability with confidence-based human fallback design
- Built shared PySpark ETL framework adopted by 4 data engineering teams
- Reduced pipeline failures by 38%
- Improved team velocity by enabling new data source onboarding via config
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