---
title: "Mohit Mehta - Staff Software Engineer - Data & Platform Services in New Delhi | Tal"
description: "Meet Mohit Mehta, Software Engineer | ex Zomato, Unbxd Inc | CS @ GaTech, DTU | Distributed systems & Deep Learning | AI Infra & ML Systems based in New Delhi. AI stack includes antigravity, claude, and codex."
image: "https://tal.club/meet/mohit-mehta/share-image"
---

# Mohit Mehta on Tal

## Headline

Software Engineer \| ex Zomato, Unbxd Inc \| CS @ GaTech, DTU \| Distributed systems & Deep Learning \| AI Infra & ML Systems

## About

Interested in distributed systems, algorithms and deep learning\. Need to get in touch \- mehtamohit987 \[@\] gmail\.com / github\.com/mohitmehta11 / mmehta66 \[@\] gatech\.edu

## Location

New Delhi

## AI stack

antigravity

claude

codex

cursor

## Work experience

Staff Software Engineer \- Data & Platform Services at Nielsen, Aug 2024 \- Present · 2 yrs 1 mo, Remote, \- Built data\-ingestion and internal\-crediting pipelines for audio measurement data, including volumetric comparisons across sources feeding downstream reporting\. \- Developed audio fingerprint\-extraction and feature\-preparation workflows supporting content identification at scale\. \- Built a self\-service data platform with a RAG\-grounded natural\-language\-to\-SQL interface — retrieving relevant table schemas, business\-glossary definitions, and past query examples to ground generation — with tools exposed as MCP servers for AI\-assistant integration, cutting ad\-hoc data\-request turnaround from∼2 days to under 30 min and lifting self\-serve query volume by ∼35%\. \- Engineered the NL\-to\-SQL system’s context window under a fixed token budget — schema relevance ranking/pruning, dynamic few\-shot example selection, and multi\-turn conversation memory for query refinement — cutting average prompt\-context size by ∼50% while improving first\-try query\-generation accuracy by ∼20%\. \- Built LLM\-based metadata enrichment \(zero/few\-shot classification over content descriptors\) for audio/content categorization, processing ∼50K records/day and improving downstream crediting categorization accuracy by ∼15% over the prior rule\-based tagging\.

Principal SWE \- ML at Noodle\.ai, Sep 2023 \- Jan 2024 · 5 mos, Remote, \- Built feature and forecast\-availability workflows for hierarchical demand\-supply forecasting across composite statistical models at each supply\-chain graph node, plus an analytics pipeline surfacing forecast performance/error metrics, variance, and per\-target\-date feature\-contribution breakdowns on dashboards for the applied data\-science org\. \- Designed a multi\-backend \(Airflow/Dagster\) ML workflow orchestrator on Kubeflow unifying dataset registration, feature engineering, experimentation, promotion\-to\-inference, and retraining into one self\-service platform supporting both statistical and neural forecasting models with varying resource requirements, with feature\-to\-inference lineage tracking for model explainability\. \- Added model\-monitoring and drift\-detection \(feature\-distribution PSI/KS\-tests via Evidently, surfaced through MLflow\) across the ensemble forecasting models plus feature\-store schema validation, catching silent data\-quality regressions before they reached production forecasts and cutting mean\-time\-to\-detect data issues from∼2 weeks to under 24 hours\.

Senior Software Engineer \- Search Infra / ML Platform & RecSys at Unbxd Inc\., A Netcore Company, Dec 2020 \- Dec 2022 · 2 yrs 1 mo, Remote, Software Engineer → Senior Software Engineer, Search Infra / ML Platform & RecSys \- Built a self\-service ML platform on Kubernetes/Kubeflow with a Dgraph\-backed config store \(algorithm inputs, CPU/memory/GPU resource templates, data\-lake/S3/GCS I/O paths per deployment\) that trained and served merchant\-specific models \(entity extraction, item similarity, personalization\) on demand for 220\+ SaaS clients, scaling to 700\+ live model deployments and 12–15 batch workflows across 350\+ multi\-region sites\. \- Orchestrated entity\-extraction pipelines on Kubeflow with model selection across conditional random fields and DNN reference architectures \(bi\-LSTM, LSTM\+char\-CNN, bi\-LSTM\+CRF, with/without highway and intent layers\) for catalog\-specific NER, and migrated production models \(Siamese\-network query\-category prediction, XGBoost\-ranker learning\-to\-rank, cross\-merchant Spark batch transforms\) from standalone Spark to recurring Kubeflow pipelines on segregated clickstream data\. \- Experimented with personalized recommendations with self\-deployed matrix\-factorization variants \(co\-occurrence, SVD, truncated\-SVD\) before shipping DNN re\-ranking \(YouTube DNN as default, Neural CF/MF, DSSM, factorization machines, sequential deep matching\) tuned via AutoML/Katib HPO, serving item\-similarity \(more\-like\-this/viewed/bought\) and search re\-ranking\. \- Optimized DNN serving via TensorRT/ONNX Runtime conversion and INT8 quantization \(∼4x smaller memory footprint\), and distilled larger re\-ranking models into smaller production variants via mixed\-precision \(FP16\) training, cutting p99 inference latency by∼40% and raising serving throughput per node by∼65% across the 700\+ model deployments\. \- Matured the ML platform with model registry/versioning and canary/shadow deployment gates for new ranking models, and enabled GPU sharing \(NVIDIA MPS/time\-slicing\) across the training cluster, raising multi\-tenant GPU utilization from ∼35% to ∼65% across 220\+ clients’ concurrent training workloads\.

Senior SDE \- Demand Experimentation & Serviceability, Post\-order Platform at Zomato, Apr 2019 \- Aug 2023 · 4 yrs 5 mos, New Delhi, Delhi, India, Senior SDE \- Post\-Order Platform \- Apr’23 \- Aug’23 \- Built the ingestion/storage architecture for a real\-time analytics service on a shared, tenant\-isolated Apache Pinot cluster \(12 nodes, 128GB/1\.9TB each\) — protobuf event\-ingestion contracts over Kafka at up to 140K events/min, serving all segments from memory with page\-cache backing and S3 deep storage over a 3\-month retention window at minute/5\-min approximation granularity\. \- Designed a custom metrics and report builder on Trino/Presto over a Hive\-Metastore\-backed S3 warehouse \(up to a year of data\) and Apache Pinot \(up to 1 month, low\-latency multi\-stage\-join previews\), letting support teams self\-serve filters, aggregates, ticket\-state\-transition views, and pinned dashboards via YAML\-defined query contracts with per\-query timeout/throughput/caching tiers\. SDE II \- Demand Experimentation & Serviceability \- Apr’19 \- Mar’20 \- break \- Modeled restaurant serviceability as a precomputed truth\-value grid on level\-15 S2 cells \(∼0\.06km2\) — keyed on \(source\-cell, dest\-cell, segment, hour\) from road\-distance/ETA and rider\-availability signals — spanning∼2B source\-destination pairs \(7K\+ candidate user cells ×3–4K candidate restaurants within 11km, 70\+ cities\), generated by a hierarchical rule engine \(City > Locality > Restaurant plus in\-time override rules\) and data\-science demand\-elasticity rules refreshed hourly across 27\+ user\-segmentation buckets, live within a∼7–9 min update SLA at T/T\+1\-hour granularity\. \- Built a custom Redis Lua operation for the read path — reverse\-lookup SISMEMBER over∼400–500 merchant\-cell keys per shard as one blocking node\-level call — fanned out in parallel across shards via a hashring synced through Predixy, collapsing a multi\-hop per\-key lookup into a single pipelined round\-trip per request and sustaining 1\.2–1\.5K rps at p99 3–4ms across∼4K candidate restaurants per request\.

## Education

Bachelor of Science \- BS, Computer Science, Delhi College of Engineering

Master of Science \- MS, Computer Science \- Computing Systems and Machine Learning, Georgia Institute of Technology

[View the original profile](https://tal.club/meet/mohit-mehta)

```json
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Person",
      "@id": "https://tal.club/meet/mohit-mehta#person",
      "name": "Mohit Mehta",
      "url": "https://tal.club/meet/mohit-mehta",
      "image": "https://tal.club/meet/mohit-mehta/profile-photo.jpg",
      "description": "Meet Mohit Mehta, Software Engineer | ex Zomato, Unbxd Inc | CS @ GaTech, DTU | Distributed systems & Deep Learning | AI Infra & ML Systems based in New Delhi. AI stack includes antigravity, claude, and codex.",
      "jobTitle": "Staff Software Engineer - Data & Platform Services",
      "worksFor": {
        "@type": "Organization",
        "name": "Nielsen"
      },
      "hasOccupation": {
        "@type": "Occupation",
        "name": "Staff Software Engineer - Data & Platform Services"
      },
      "homeLocation": {
        "@type": "Place",
        "name": "New Delhi"
      },
      "alumniOf": [
        {
          "@type": "EducationalOrganization",
          "name": "Delhi College of Engineering"
        },
        {
          "@type": "EducationalOrganization",
          "name": "Georgia Institute of Technology"
        }
      ],
      "knowsAbout": [
        "antigravity",
        "claude",
        "codex",
        "cursor"
      ],
      "mainEntityOfPage": {
        "@id": "https://tal.club/meet/mohit-mehta#webpage"
      }
    },
    {
      "@type": "ProfilePage",
      "@id": "https://tal.club/meet/mohit-mehta#webpage",
      "url": "https://tal.club/meet/mohit-mehta",
      "name": "Mohit Mehta on Tal",
      "description": "Meet Mohit Mehta, Software Engineer | ex Zomato, Unbxd Inc | CS @ GaTech, DTU | Distributed systems & Deep Learning | AI Infra & ML Systems based in New Delhi. AI stack includes antigravity, claude, and codex.",
      "dateModified": "2026-08-06T17:18:08.053Z",
      "isPartOf": {
        "@id": "https://tal.club/#website"
      },
      "mainEntity": {
        "@id": "https://tal.club/meet/mohit-mehta#person"
      },
      "primaryImageOfPage": {
        "@type": "ImageObject",
        "url": "https://tal.club/meet/mohit-mehta/profile-photo.jpg",
        "contentUrl": "https://tal.club/meet/mohit-mehta/profile-photo.jpg",
        "caption": "Mohit Mehta profile photo on Tal"
      },
      "inLanguage": "en-IN"
    }
  ]
}
```