Experience

Building a standalone dataset-quality evaluation toolkit for AIKosh (under MeitY's IndiaAI Mission) — bridging the ICMR MIDAS 2.0 framework gap, slated for production integration by internship end.

• Metadata Intake: Engineered an 8-step wizard with pre-signed URLs for direct-to-storage uploads, bypassing the backend to enhance security and reduce server load.
• Assessment Engine: Architected an asynchronous, parallelized scoring engine to evaluate datasets across 15 government-defined quality domains, generating Composite Quality and Privacy Risk scores for automated release eligibility (Open/Controlled/Restricted).
• Integration: Built automated multi-format reporting (JSON/HTML/PDF) and webhook APIs, enabling AIKosh to auto-ingest verified dataset metadata upon assessment completion.

Building a self-contained, fully offline GIS platform enabling classified defence environments with no internet access to securely upload, process, and visualize geospatial map data.

• Ingestion Pipelines: Built automated ETL pipelines for vector (Shapefiles to PostGIS with reprojection/indexing) and raster data (GeoTIFFs auto-published via REST), eliminating manual GIS server setups.
• Serving Layer & Infra: Enabled real-time map-tile delivery to browser clients via a containerized 4-service microservices backend, securely deployed on a firewalled RHEL environment for fully offline, classified operations.

Evaluated enterprise AI toolchains and shipped production integration tests on live Salesforce CRM infrastructure.

• Benchmarking: Evaluated Vanna.ai, LlamaIndex, LangChain for NL-to-SQL, and audio frameworks (Librosa, PyDub) for feature extraction; identified that agent architecture outweighs model choice for multi-table reasoning.
• Salesforce AI Platform: Built Apex REST services and SOAP integrations; conducted prompt engineering experiments on live CRM data using Einstein AI and Agentforce.