---
title: "Harit Mohan - Backend Software Engineer (SDE-1) in Bengaluru | Tal"
description: "Meet Harit Mohan, Backend Software Engineer (SDE-1) @ HealthifyMe | Ex-Amazon SDE | Python · Django · FastAPI · AWS · Kafka | Production LLM systems: grounded AI diet plans ~900/day, two-way EHR sync for ~1M users based in Bengaluru. AI stack includes anthropic apis, claude code, and codex. Explore..."
image: "https://tal.club/meet/harit-mohan/share-image"
---

# Harit Mohan on Tal

## Headline

Backend Software Engineer \(SDE\-1\) @ HealthifyMe \| Ex\-Amazon SDE \| Python · Django · FastAPI · AWS · Kafka \| Production LLM systems: grounded AI diet plans \~900/day, two\-way EHR sync for \~1M users

## About

Backend engineer \(3\+ years\) building production systems where reliability and AI meet: Python, Django, FastAPI, Celery, GraphQL, Kafka, AWS, MySQL, MongoDB, and LLM engineering \(OpenAI GPT, Gemini / Vertex AI, LiteLLM\)\. Backend SDE\-1 at HealthifyMe in Bengaluru; previously SDE at Amazon\. Highlights: • AI diet plans: designed a 3\-layer LLM\-grounding pipeline \(macro recomputation, food\-DB resolution, algebraic serving solver\) that makes unreliable LLM output production\-safe — \~900 plans/day to \~700 daily active users, each fully customized plan delivered in \~1 minute\. • Migrated generation to an async FastAPI orchestrator: \~5s faster, zero\-incident phased rollout \(5/25/60/100%\) verified by a 1e\-9 golden\-parity suite and shadow\-run comparison\. • Platform sync: built a loop\-safe, idempotent two\-way EHR sync \(8 health\-log domains over GraphQL \+ webhooks\) that enabled \~1M users across two health\-tech platforms, and designed UserLogEventRegistry, a pub\-sub framework now reused by multiple services\. • GLP\-1 automation: replaced a daily manual ops task with a hands\-off pipeline validating \~100 pen orders/day against doctor prescriptions — deterministic parser with an extraction\-only LLM fallback, idempotent quota\-safe third\-party export\. • At Amazon: designed and built an LWA \(Login with Amazon\) SDK onboarding pipeline in AWS CDK — the standardized path for Kepler app onboarding\. I care about the unglamorous parts that make systems trustworthy: idempotency, backward compatibility, phased rollouts, and deterministic validation over LLM trust\.

## Location

Bengaluru

## AI stack

anthropic apis

claude code

codex

grok

hermes agent

mcp

## Work experience

Backend Software Engineer \(SDE\-1\) at Healthify, Mar 2025 \- Present · 1 y 5 mo, Bengaluru, Karnataka, India, Tech: Python, Django, Celery, FastAPI, GraphQL, MySQL, MongoDB, LiteLLM \(OpenAI GPT\-4o, Gemini / Vertex AI\) • Designed a 3\-layer LLM\-grounding pipeline \(macro recomputation, food\-DB resolution, algebraic serving solver\) guaranteeing nutritionally valid AI diet plans — \~900 plans/day to \~700 daily active users, delivered in \~1 minute\. • Reduced diet\-plan generation time by \~5s by moving I/O\-bound LLM calls off Celery into an async FastAPI orchestrator, via a zero\-incident phased rollout backed by a 1e\-9 golden\-parity suite\. • Enabled \~1M users across two health\-tech platforms by building a loop\-safe, idempotent two\-way EHR sync across 8 health\-log domains over GraphQL and webhooks\. • Designed UserLogEventRegistry, a reusable pub\-sub framework that decouples user\-log emission from consumption — adopted by Ria AI, coach highlights, and Healthie sync\. • Automated daily GLP\-1 order\-vs\-prescription validation \(\~100 orders/day\) with a deterministic parser plus extraction\-only LLM fallback and an idempotent, quota\-safe Google Sheets export\. • Fully owned the clinical charting\-notes \+ AI\-summary feature: webhook ingest, GraphQL fetch, MongoDB storage, LLM summaries, coach notifications, Ria AI context injection\. • Reorganized the Coach\-Copilot v2 API into 6 purpose\-specific REST endpoints and built a provider\-aware multimodal content pipeline \(150\+ MIME types, OpenAI \+ Vertex AI\)\. • Owned the LLM model lifecycle: migrated deprecated Gemini/GPT models, upgraded the multi\-provider LiteLLM integration, mitigated 429/rate\-limit failures before outages\.

Software Development Engineer at Amazon, Apr 2024 \- Feb 2025 · 11 mos, Bengaluru, Karnataka, India, Tech: AWS, AWS CDK, C\+\+, Python, S3, RDS • Designed and built the LWA \(Login with Amazon\) SDK onboarding pipeline in AWS CDK, making the SDK the standardized path for Kepler app onboarding\. • Ran the onboarding pipeline at 99\.9% uptime with 100% compliance with Kepler onboarding requirements for SDK externalization\. • Integrated CDK with LivePipeline Template \(LPT\), enabling seamless, hands\-off production deployments\. • Implemented a comprehensive error\-handling strategy across services — unhandled exceptions down \~95%, MTTR for critical issues down \~60%\. • Worked across C\+\+ and Python services on AWS infrastructure \(S3, RDS\), shipping all infrastructure as code with CDK\.

Backend Development Engineer at Tailnode Technology, Sep 2023 \- Mar 2024 · 7 mos, Remote, Tech: Node\.js, TypeScript, Cassandra DB, Kafka, MySQL, Jenkins • Built the backend for the employee self\-onboarding system \(web \+ mobile\), cutting manual onboarding effort by \~50% and improving time\-to\-productivity for new hires\. • Resolved a critical MySQL race condition that produced duplicate employee codes by routing task processing through Kafka for sequential ordering\. • Integrated secure OAuth2 Google Login for user authentication\. • Hardened Node\.js APIs with comprehensive exception handling, cutting production failures by \~30% within the first month\. • Owned bug fixes and on\-call support — rapid incident response and hotfix deployments maintaining \~90% service availability\.

Software Development Engineer Intern at Amazon, Feb 2023 \- Jun 2023 · 5 mos, Bengaluru, Karnataka, India, Tech: AWS \(Redshift, EC2, S3, RDS, Beanstalk, CloudWatch\), Python, Pandas, Glue, QuickSight • Built a cost\-estimation tool for Kindle Device Metrics \(KDM\) using Python, Pandas, and AWS Glue that surfaced underutilized AWS resources — driving a \~20% reduction in internal AWS bills through decommissioning\. • Collected and maintained AWS usage data across Redshift, EC2, S3, RDS, Beanstalk, and CloudWatch, and built a dashboard for querying detailed service and resource information\. • Transformed large datasets into analysis\-ready formats with Pandas, enabling resource cost analysis and optimization\.

Python Development Intern at TechWhoop, Jan 2022 \- Feb 2022 · 2 mos, Remote, Tech: Django, Python, GCP, Google Sheets API, Selenium, Bootstrap • Automated a manual Excel\-based operations workflow end to end using Python/Django and the Google Sheets API on GCP, eliminating recurring manual ops effort and adding a daily run\-results dashboard\. • Built Django backends and Bootstrap front ends for three websites, integrating the Google Sheets API for result storage and the 2Captcha API over HTTP\. • Kept crawler bots running reliably with rotating proxies to prevent bans\.

## Education

Bachelor of Technology \- BTech, Computer Science, jaypee institute of information technology

Silverline Prestige School

## Public links

- github: https://github\.com/hashharit

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

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