Pawan Jain

Software Engineer

Alwar, Rajasthan, India4 yrs 6 mos experience

Highly Stable

Key Highlights

Led API migration reducing latency by 47%
Scaled chatbot platform for 15+ enterprise clients
Built real-time event-driven pipelines with AWS

Stackforce AI infers this person is a Backend-focused Software Engineer in the SaaS industry.

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Skills

Core Skills

Backend DevelopmentCloud ComputingFrontend Development

Other Skills

APSchedulerAWS CDKAWS CloudFormationAWS LambdaAWS S3AWS SDKAWS ServicesAgile MethodologiesAmazon Simple Notification Service (SNS)Apache KafkaCeleryContinuous Integration (CI)Data AnalyticsData VisualizationDatabases

About

I'm a Software Engineer with over 4 years of experience building and scaling AI-powered systems and cloud-based applications. I work across the full stack but focus mainly on backend development using Python (FastAPI, Flask, Django), Golang, and TypeScript (Node.js, React). At GoDaddy, I’ve led projects that improved system performance, reliability, and scalability—like redesigning APIs using DynamoDB with optimized indexes, building real-time event-driven pipelines with AWS CDK, and creating microservices that handle persistent memory and vector search for LLM agents. Previously, at Innovaccer, I helped scale a chatbot platform for 15+ enterprise clients, moved workloads to async to reduce CPU usage, and improved response speeds by over 50%. I’ve worked with a wide range of technologies: AWS (Lambda, S3, DynamoDB, Bedrock, CDK), MongoDB, Redis, Postgres, OpenSearch, and message queues like Kafka and SQS. I'm also comfortable with tools like Cohere, Snowflake, and various observability tools (CloudWatch, Slack alerts). I enjoy solving complex problems, working on fast-moving teams, and delivering systems that perform well in production. Always curious, always learning.

Experience

Godaddy

Software Development Engineer - II

Oct 2024 – Present · 1 yr 5 mos · Gurugram, Haryana, India · Remote

Led the migration of the Agent State Memory API to an optimized DynamoDB schema using AWS SDK v2, with carefully designed GSIs and LSIs. Implemented within a Golang (Chi) microservices environment, resulting in a 47% drop in latency and enhanced system scalability.
Built a real-time event-driven pipeline using AWS CDK (TypeScript) with DynamoDB, Kinesis, Lambda, and DocumentDB, adding 10K conversation records daily. Achieved <100ms query latency, 99.9% event processing success rate, and auto-alerting via CloudWatch, SQS, SNS, and Slack. Designed full infra with retries, DLQ handling, and monitoring.
Led the design and implementation of the chatbot conversation experience using React, Redux, React Query, and TypeScript. Optimized API caching and component rendering, reducing message load time by 35%
Contributed to Agent Platform leveraging OpenSearch (AOSS) to enable scalable document chunking, vector indexing, and semantic retrieval. Integrated AWS services with built-in guardrails for secure, controlled KB operations—powering 1K+ agent deployments, and ensuring 99.9% uptime aligned with SLA commitments in last 5 months.
Moving to a FastAPI microservice integrating AWS Bedrock KB, DynamoDB, and S3 for scalable persistent memory and knowledge base of LLM Agents. Aiming for 30% better top-5 search accuracy with Cohere v3 + ReRank 3.5, 30% lower ops overhead, and 2x faster file search.

GolangDynamoDBAWS CDKReactTypeScriptOpenSearch+2

Innovaccer

Software Development Engineer - I

Aug 2021 – Sep 2024 · 3 yrs 1 mo · Noida, Uttar Pradesh, India · On-site

Engineered and deployed RESTful APIs using FastAPI (Python 3) for SARA Chatbot—an LLM-powered SaaS platform—running on Kubernetes (K8s) with Helm for scalable, secure, and multi-tenant deployments.
Designed and implemented a distributed scheduler microservice using APScheduler, Celery, and Redis, reducing background task latency from 9s to 0.3s, optimizing task execution throughput under load.
Scaled chatbot infrastructure to support 15+ enterprise clients, leveraging a hybrid architecture (FastAPI + Celery + Redis) to improve message turnaround time by 34% and ensure low-latency, bidirectional communication.
Integrated polyglot data stores—MongoDB for real-time conversations, AWS S3 for dynamic KB ingestion, Redis for session management, and Snowflake for analytics—achieving 99.9% uptime and a 56% boost in response speed.
Migrated sync pipelines to fully async, adopting aiobotocore, aioredis, httpx, aiohttp, and motor, resulting in a 56% drop in CPU usage during peak workloads and improved system throughput.

FastAPICeleryRedisMongoDBAWS S3Snowflake+2

Delhivery

Data Science Intern

Jan 2021 – Aug 2021 · 7 mos

Worked with the BI team to increase coverage of ETA for packages and delivering alerts for all major hubs if there is any spike in volume
Created a scoring model for addresses based on validity by using various attributes present in addresses and historic data
Build a pre-junk predictor for addresses based on various recognised patterns
Tech stacks -
Python, Jupyter, MySQL, Sagemaker, RDS, S3, Google Maps API

PythonMySQLSagemakerGoogle Maps API

Fractal

Data Science Intern

May 2020 – Jul 2020 · 2 mos

Analyzed Fractal employees project allocation data to examine the error in Full-Time Engagement allocation in departments and units
Visualized variable assisting in intelligent allocation by correlating billed and unbilled days, revenue utilization, and organizational units