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Naga Gayatri Bandaru
Mid-level AI/ML Engineer specializing in MLOps and production ML systems
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About
Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.
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Key Strengths
- Built real-time recommendation engine serving ~2M monthly users; reduced feature latency from 5 min to 30 sec
- Drove measurable business impact: +15% cross-sell revenue and +12% engagement via real-time architecture migration
- Zero-downtime migration using dual-write and PostgreSQL cross-verification for consistency
- Designed HIPAA-compliant multimodal clinical AI system (DICOM + EHR) integrated into clinician workflow
- End-to-end ownership of MLOps/serving: MLflow + Airflow + Kubernetes supporting 10k+ daily predictions
- Improved clinician adoption with explainability (SHAP/LIME), increasing trust/adoption by ~40%
- Operational rigor: monitoring/observability (Prometheus/Grafana/CloudWatch), drift detection reducing response time by 65%
- Database performance tuning: indexing + query refactor yielding ~70% query time reduction
- Reliability engineering in distributed pipelines (autoscaling, retries, validation gates, OOM mitigation via Spark tuning)
Reference Highlights
- did well as an intern on ML sentiment analysis project
- good Python coding (per tech lead feedback)
- worked well paired with a full-stack AI engineer
- performed well under tech lead supervision
- successfully integrated GPT into sentiment analysis app
- positive end-user test feedback on the app
- Contributed effectively as part of a collaborative intern team
- Able to learn many components quickly in a resource-constrained environment
- Helped deliver foundational DevOps/MLOps infrastructure for the company
- Team achieved significant progress (0 to 16 models working; ~80% of targeted models brought up)
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