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Rizwana Shaik
Mid-level Full-Stack Software Engineer specializing in cloud-native apps and AI copilots
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About
Internship project building and deploying a LLaMA-based, RAG-enabled copilot inside a Professional Services Automation platform, enabling natural-language navigation, text-to-SQL reporting, and project/resource/budget insights across multiple modules. Addressed real production issues like context drift and vague queries with hybrid search, metadata enrichment, and an intent classification/rewriting layer, orchestrated via Apache Airflow—ultimately cutting PMO reporting time by 40%.
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Key Strengths
- Built and deployed an LLM copilot in a PSA platform across multiple modules (Projects, Clients, Vendors, Resources, Time entries)
- Designed text-to-SQL + RAG system returning grounded explanations and charts from database data
- Solved context drift at scale using metadata enrichment and hybrid search to improve relevance
- Improved handling of vague user queries via intent detection/query classification and structured task rewriting
- Production-minded agent design: success metrics, structured schemas, offline evals with real queries + human scoring, and monitoring (latency/error rates)
- Implemented safe operation patterns: grounding, RBAC, and fallbacks to reduce hallucinations
- Used Apache Airflow to orchestrate ingestion, embedding refresh, vector store maintenance, and cache warmup for real-time copilot features
- Collaborated with non-technical PMO stakeholder; reduced reporting time by 40% through iterative feedback and training
- Designed and built Flask microservices for a PSA platform (analytics, resource allocation, AI copilot)
- Performance tuning with indexing, normalization, selective loading, batching, and caching
- Reliability engineering for microservices (health checks, retries/timeouts, load balancing, horizontal scaling, centralized logging)
- Strong SQL performance workflow using PostgreSQL EXPLAIN ANALYZE and ongoing monitoring via dashboards
- Production AI integration using LLM APIs with RAG, embeddings/vector DB, and async processing to manage latency
- Multi-tenant data isolation implementation (row-level tenant ID enforcement, schema-per-tenant, automated tests to prevent cross-tenant leakage)
- High-throughput background task optimization using Celery with monitoring/alerting (Flower, structured logs, metrics)
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