Freelancers using Kafka in IndiaFreelancers using Kafka in India
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Drivyesh Modi's avatar
Drivyesh Modi
India
Growth is the only constant to achieve Perfection
Growth is the only constant to achieve Perfection
Cover image for Technical Mock interviews and coaching for big firms
Technical Mock interviews and coaching for big firms
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25
Cover image for Java Project and Bug Fixing
Java Project and Bug Fixing
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21
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Java Application Project
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25
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Kafka(3)
Aishwary Dhare's avatar
Aishwary Dhare
proBengaluru, India
Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
    5.0
    Rating
    8
    Followers
Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
Cover image for Global FinTech Development for MishiPay
Global FinTech Development for MishiPay
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14
Cover image for High-Performance Cryptocurrency Trading Engine Development
High-Performance Cryptocurrency Trading Engine Development
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12
Cover image for Migration of ETL Pipelines to Apache Airflow
Migration of ETL Pipelines to Apache Airflow
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18
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Development of PayLater & PayLater Business Apps
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24
Kafka(2)
Sreeram Melarkode's avatar
Sreeram Melarkode
Thane West, India
Wordsmith for hire: Writing, research, consulting
    5.0
    Rating
    7
    Followers
Wordsmith for hire: Writing, research, consulting
Cover image for Near Real Time Data WareHouse
Near Real Time Data WareHouse
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21
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Solution Consulting
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18
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Mobile Device Policy
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25
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Kafka(1)
Soumabha Roy Chaudhuri's avatar
Soumabha Roy Chaudhuri
Bengaluru, India
Fractional CTO | Software Architect | SaaS | AI Agents
Fractional CTO | Software Architect | SaaS | AI Agents
Cover image for 10x throughput · Kafka ·
10x throughput · Kafka · 13 social channels · built for viral-scale message bursts Redesigned an enterprise social platform's data layer into an event-driven architecture on Apache Kafka, ingesting and analysing real-time social data from 13 channels. Sized for viral-scale bursts of 250–1,000 messages/minute per customer, with hybrid delivery paths: streaming (~15–20s average latency) and polling (~2 min), with a worst-case 5-minute latency ceiling. Also set architecture standards across application, data, and AI, and rebuilt the React/TypeScript frontend for multilingual workspaces with automated WCAG 2.2 / ADA Section 508 accessibility checks. End-to-end throughput improved 10x. Confidential client engagement.
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25
Cover image for Cold start: 17 min →
Cold start: 17 min → under 3 min. Shard loading: 16 min → 50 sec. Serving cost: ~1/6 of a managed SageMaker equivalent. Performance-engineered a LLaVA vision-language deployment on AWS as part of a specialised strike team, with the full write-up published on LinkedIn (linked under Completed Work). Reduced end-to-end cold start from ~17 minutes to under 3 (66%) via custom Deep Learning AMIs (−32% immediately), ephemeral-storage model caching with parallel aria2c downloads (checkpoint loading 16 min → 50 sec), and 4-bit NF4 quantization (shard loading 41s → 18s). Along the way, diagnosed a storage-path bug where checkpoint shards kept re-downloading despite being baked into the AMI.
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50
Cover image for Cut shipping time 40% with
Cut shipping time 40% with a production multi-agent system built around governance, not demos. Confidential client engagement. Designed and deployed a multi-agent system covering the full product lifecycle: PRD generation → system design → multi-repo execution → code review → compliance/security checks → release management. Shipping velocity improved 40% across cross-functional teams. Built on a Backstage-based engineering knowledge base with MCP connectors to JIRA, GitHub, Slack, and Teams; LLM workloads on OpenAI and Anthropic via A2A-style agent orchestration, implemented in Python/Bash tooling. Shipped with an enterprise AI governance layer: hallucination detection with knowledge-base remediation, model drift monitoring, and audit logging for regulated environments.
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Cover image for 90M registered users · 17
90M registered users · 17 countries · 99.99% uptime · $451M disbursed via DBT Led Everwell Hub from a monolith to a horizontally scalable microservices platform (Java/Python, Docker/Kubernetes, GCP, CI/CD, IaC). As chief architect I personally designed the service decomposition; as CTO I led ~50 engineers plus system-integrator partners and the WHO/Ministry of Health partnership. Deployed across 17 countries and scaled India's national TB platform (Ni-kshay) to ~90M registered patients and staff, with 6 lakh+ direct users, at 99.99% uptime, sub-second P95 latency, and 2h RTO / 4h RPO disaster recovery. The payments module disbursed $451M through direct benefit transfers. Released the platform as MIT open source with full wiki, runbooks, and API contracts — adopted by partner health systems since.
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Kafka(1)
ANIMESH SINGH's avatar
ANIMESH SINGH
Delhi, India
Data & AI Engineer
Data & AI Engineer
Cover image for Problem: Many organizations still process invoices
Problem: Many organizations still process invoices manually by reading PDF documents and entering key details (invoice number, vendor, amount, etc.) into systems. This process is slow, error-prone, and difficult to scale, and it also makes it harder to detect duplicate invoices or incorrect totals. Solution: This project builds an automated invoice processing pipeline that converts uploaded invoice PDFs into structured data. It uses OCR to extract text, LLMs to identify invoice fields, validation checks to ensure correctness, and Kafka-based event streaming to manage the processing pipeline. The extracted data is stored in PostgreSQL and visualized through a dashboard, enabling faster, scalable, and more reliable invoice processing.
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Cover image for Problem Statement Urban traffic management systems
Problem Statement Urban traffic management systems lack real-time, integrated data combining traffic conditions with weather patterns. This results in poor routing decisions, delayed emergency responses, and inefficient traffic flow management. Solution Developed a comprehensive real-time ETL pipeline that integrates traffic APIs and weather data sources, processes millions of data points, and delivers actionable insights through interactive dashboards for traffic management and route optimization.
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Cover image for A fully local RAG pipeline
A fully local RAG pipeline that transforms your PDFs into a queryable knowledge base using FAISS vector search and Ollama LLMs. No cloud, no API keys - just private, grounded document intelligence running entirely on your machine.
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73
Cover image for A fully Dockerized real-time IoT
A fully Dockerized real-time IoT ETL pipeline that simulates device telemetry, processes events through MQTT and Kafka, orchestrates workflows with Airflow, and delivers real-time alerts and insights to CRM systems with monitoring via Grafana and Loki.
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Kafka(2)
kothakapu saikrishna reddy's avatar
kothakapu saikrishna reddy
Hyderabad, India
Java Backend Developer | Building Scalable REST APIs
New to Contra
Java Backend Developer | Building Scalable REST APIs
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banking-platform-microservices
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0
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Apply for once
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0
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Complete-Java-MultiThreading-Concepts
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0
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applyforonce
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0
Kafka(1)
CS48 Sagar gaud's avatar
CS48 Sagar gaud
Agra, India
AI Engineer | 7 mo exp in ML, LLMs, debugging, US clients.
AI Engineer | 7 mo exp in ML, LLMs, debugging, US clients.
Cover image for Lillypad Coding Project Completion
Lillypad Coding Project Completion
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12
Cover image for Trending Technologies & The Vision of CodingWorkX
Trending Technologies & The Vision of CodingWorkX
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11
Cover image for User Engagement for Eatsure’s Food Delivery App
User Engagement for Eatsure’s Food Delivery App
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7
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Kafka(1)