Aayush Tiwari

Helped move a sanctions-screening platform from proof of concept toward a scalable, configuration-driven system spanning engineering design, processing workflows, and operational tooling.

• Participated in system-design proposals with engineering and project stakeholders, translating operational requirements into architecture and workflow.
• Built and iteratively evolved a Python, FastAPI, React, and TypeScript platform capable of processing files with 80M+ records across DEV, UAT, and pre-production environments.
• Implemented configurable ingestion, validation, normalisation, clustering, candidate generation, rules, and decision-merging stages.
• Built run monitoring, warnings, timelines, and paginated outputs for the operational dashboard.
• Added run-scoped isolation, bearer authentication, RBAC, audit logging, and Pytest-based testing.

Refactored a Node.js backend for an AI counselling product, separating inference from session state and simplifying a four-stage conversational workflow.

• Reduced inter-service coupling by approximately 40% and code redundancy by approximately 30%.
• Implemented keyword-triggered crisis routing with a routing decision under 100 ms.
• Built a profile-aware returning-user workflow that removed redundant onboarding for approximately 60% of repeat sessions.

Built SQL-based ETL and analytics workflows that consolidated operational data into a normalized warehouse and decision-ready Power BI reporting.

• Integrated 5+ source systems into a normalized, fact-and-dimension data warehouse.
• Delivered 3+ Power BI dashboards with 15+ custom DAX measures for revenue, churn, and funnel analysis.
• Reduced ad-hoc reporting turnaround by approximately 35% and improved refresh time by approximately 25% through query and model optimization.

A research-oriented Indian mutual-fund ranking system that combines public data, role-specialised agents, deterministic consensus behavior, and evaluation tooling.

Python / Ollama / Qwen2.5-7B / Gemini / Pandas

An incident-response platform that correlates metrics, logs, traces, topology, and change evidence into auditable root-cause rankings and evidence-cited diagnoses.

Python / FastAPI / Next.js / PostgreSQL / pgvector

A collaborative Kafka and Spark Structured Streaming pipeline benchmarked for high-throughput fraud scoring with a compact feature set.

Apache Kafka / PySpark / Spark Structured Streaming / Spark MLlib / NumPy