---
title: "Vineet Sharma - MLOps Engineer in Bengaluru | Tal"
description: "Meet Vineet Sharma, MLOps and Data Engineering | IIIT based in Bengaluru. AI stack includes github copilot, langchain, and langgraph. Explore public work links including github."
image: "https://tal.club/meet/vineet-sharma-1/share-image"
---

# Vineet Sharma on Tal

## Headline

MLOps and Data Engineering \| IIIT

## About

MLOps / Data Platform Engineer with 5\+ years of experience designing and scaling ML infrastructure and data pipelines on AWS\. Expertise in building event\-driven distributed systems and ML systems using Kafka, deploying containerized ML workloads on Kubernetes, and managing end\-to\-end ML lifecycle with Sagemaker and DVC\. Strong background in infrastructure automation using Terraform and building production\-grade observability systems\.\. Passionate about problem\-solving and finding better solutions for the existing one, I am an avid team player and love contributing to impactful projects\. Tech Skills Languages: Python, SQL MLOps: MLflow, Feast, DVC, AWS SageMaker, GenAI, LLM, LangGraph, LangChain Data: Airflow, Kafka, Spark, Snowflake, dbt, ETL/ELT DevOps: Docker, Kubernetes\(EKS\), Terraform,Helm, Github Actions, ArgoCD, SLI, SLO, SLA Observability: Prometheus, Datadog, FluentBit Databases: Postgres, DynamoDB Cloud AWS: S3, RDS, ECS, EC2, DynamoDB, OpenSearch, SQS, IAM, Lambda, Eventbridge, Lambda, CodeBuild, EventBridge Other: UML Modelling

## Location

Bengaluru

## AI stack

github copilot

langchain

langgraph

notion

## Work experience

MLOps Engineer at ResMed, Feb 2024 \- Present · 2 yrs 7 mos, Bengaluru, Karnataka, India, ● Deployed consumer facing RAG based chatbot \- Dawn \- in production ○ Setup design to load test 1k users requests for EKS based model API and implement load testing using locust ○ Tune parameters for API deployment for scaling pod replicas and unicorn workers for the API ○ Deployed AWS Opensearch as vector store and setup hybrid search for matching relevant docs from vector store ○ Designed and deployed scalable SLM inference APIs on EKS ○ Setup canary deployment using Argo Rollout for the API to new version release ● Setup and deploy ML Platform in AMR and EU region for nonprod and prod to host 2000\+ user services ○ Setup IaC and CI/CD github actions to deploy EKS clusters per environment ○ Deployed AWS Privatelink connection between On\-premise datacenter and AWS account through hybrid DNS ○ Setup and deployed using ArgoCD helm charts for Prometheus, Inferentia Operator and Nvidia GPU operator ○ Setup Kyverno policies to enforce tags on workloads running on platform ○ Debug platform and user workload issues ● Led design and deployment of EKS based ML platform monitoring layer over Datadog by managing a team of 3 engineers\. ○ Setup reusable terraform module which allows creating new monitor in datadog within 15 mins ○ Setup SLO monitors for production ML workload for SLIs

Data Engineer II\(MLOps\+Data\) at Episource, Apr 2023 \- Jan 2024 · 10 mos, Mumbai, Maharashtra, India, Working in NLP & Data Science Team where my work revolves around MLOps and Platform Engineering ● Deployed model real time monitoring layer for multi\-model ML system with Snowflake as data warehouse and analytics layer for 2M\+ data points per hour; provide insights through SQL reporting to reduce overall pipeline time by 30% ● Deployed Kafka consumers as microserverice in EKS to get model inference in event driven pipeline ● Deployed extractive ML model on AWS Sagemaker and tuned deployment parameters to support 100k ML API request / min

Data Engineer I at Episource, Apr 2022 \- Mar 2023 · 1 yr, Mumbai, Maharashtra, India, \- Event driven multi\-model pipeline \* Deployed SQS broker and Celery workers\+infra in EKS to autoscale with SQS queue length \* Deployed Airflow DAG in Kubernetes executor environment to ingest 22K messages in parallel into SQS in 5 mins \- Deployed infrastructure for ML Pipeline \* Setup Batch as IaC with optimal scheduling policy to run multiple batch jobs in parallel \* Setup Lambda as IaC with multiple validation layers to verify and pre\-process data \* Setup DynamoDB as IaC with autoscaling for read/write capacity \* Refactored existing workflows to build custom packer\-based AMIs \* Collaborated with geographically distributed team \- PoC on AWS EventBridge \* Setup eventbus, rules, triggers and IAM policy as IaC \* Tested target trigger via put event script \- PoC on AWS Opensearch cluster \* Setup infrastructure as IaC with optimal storage, node and indexing strategy for Opensearch \* Added script to index \~1million rows per minute \* Load tested Opensearch cluster to monitor key performance indicators \* Developed domain access polices \- Tech Stack : Python, Celery, Pandas, Git, Terraform, API Load Testing, Airflow, CI/CD, RDBMS, SQL, AWS \- Opensearch, Lambda, IAM, EKS, EventBridge, DynamoDB, Batch, CodeBuild, RDS

Associate Data Engineer at Episource, Jul 2021 \- Mar 2022 · 9 mos, \- Developed multi\-repo documentation microservice from scratch and deployed on AWS \* Designed and implemented CI/CD workflows for continuous integration and validation of documentation \* Researched and configured Sphinx to generate static site from MD, rST files and auto\-generate API documentation from code \* Provisioned infrastructure resources on AWS, using Terraform, to run docserver as containerized service on ECS \* Documented the service \* Generated Sphinx based documentation of few repositories and integrated with the service\. \- Deployed DAG on Kubernetes Executor based Airflow environment \* Developed task script to query Athena \* Developed task script to copy 1200 files per minute in parallel in S3 \* Deployed React\-Flask based dashboard to provide input to DAG via Kafka based pub/sub layer \* Developed scripts to asynchronously query multiple, nested endpoints in Airflow REST API \* Documented the tool with UMLs \- Deployed Fluent\-bit as Daemonset in EKS cluster for routing logs to CloudWatch \* Documented the log agent \- Developed project template for Airflow DAG to automate DAG template generation through CLI \* Documented the CLI tool \- Migrated apps running in ECS to EKS with argo \- Tech Stack : Airflow, Kubernetes, Terraform, Docker, Kafka, Argo, GitHub Actions, Sphinx, Nginx, React, Flask, PyTest, UML Modelling, DVC, AWS \- ECS, EKS, EC2\-Autoscaling, Athena, S3, CloudWatch, ECR, KMS

Software Development Intern at The Solar Labs, May 2019 \- Jul 2019 · 3 mos, Noida, Uttar Pradesh, India, \- Product was developed for Government of Maharashtra to analyse rooftop solar potential of the state\. \- Researched, designed and developed the GIS backend architecture, DB schema and APIs \- Extensively used ArcGISJS API for geocoding and reverse\-geocoding, pin\-pointing a location, tracing an area, 3D visualization of shapefile data and to find intersecting shape\-ids under an traced id \- Developed a script to automate shapefile data addition into database \- Developed and designed intuitive UI \- Took complete ownership of the tool starting from designing from scratch to development \- Tech Stack : Django, Django Rest Framework, Postgres, VueJS, UML Modelling, GIS extensions for Django, Postgres and ArcGIS

## Education

Bachelor of Technology \- BTech, Computer Science and Engineering, Indian Institute of Information Technology, SriCity

## Public links

- github: https://github\.com/vineet\-sharma29

[View the original profile](https://tal.club/meet/vineet-sharma-1)

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