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Narayana Koushik kancharla
Intern Data Scientist specializing in Generative AI and NLP
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About
Backend/AI engineer with internship experience building an AI-powered financial insights platform (FastAPI, Redis, BigQuery) and prior HCL experience leading a monolith-to-microservices refactor (Flask, Kafka) using blue-green deployments. Demonstrates strong performance/security focus (OAuth/JWT/RBAC, encryption) and measurable impact on latency, downtime, and ML model reliability; MVP was submitted to Google’s accelerator program.
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Key Strengths
- Designed scalable, low-latency FastAPI backend using Redis + BigQuery; reduced response time ~40%
- Built data validation layer with TensorFlow Data Validation to catch anomalies and prevent schema/latency issues
- Improved API performance and stability via async processing and Redis key-expiration tuning (avg latency ~1.8s to ~1.0s)
- Led monolith-to-microservices refactor using Flask and Kafka with blue-green deployment and versioned APIs; reduced downtime 62%
- Implemented production security: Google OAuth, JWT, RBAC via dependency injection, query-level row security, AES encryption, HTTPS/CORS
- Identified ML edge case (neutral misclassified as positive); improved sentiment accuracy 78% to 85% using stratified sampling + confidence thresholds
- Built and deployed a production LLM-based personalized AI tutor (financial education) with citations/safety focus
- Performance optimization on Cloud Run (request batching, TTL caching, warm pool, token streaming) reducing latency from ~2.8s to ~1.1s
- Designs reliable AI workflows with SLAs/KPIs, modular components, and robust testing (unit/integration, synthetic data)
- Experienced orchestrating ML/GenAI pipelines with Airflow and AWS Step Functions (dependency management, retries, alerting, scalable components)
- Strong model/retrieval selection approach (dense vs sparse vs hybrid; cost/latency tradeoffs; prompting iteration)
- Effective collaboration with non-technical stakeholders via workshops, prototypes, and dashboards; aligned AI objectives to domain goals
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