Freelancers using PySpark

Freelancers using PySparkFreelancers using PySpark

Projects People

Copenhagen, Denmark

Optimising businesses with data and business intelligence.

18: Followers

Optimising businesses with data and business intelligence.

Cover image for Creating a Dimensional Model (Star Schema) in Databricks

Creating a Dimensional Model (Star Schema) in Databricks

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Cover image for Telco Business Intelligence Implementation

Telco Business Intelligence Implementation

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15

Cover image for Sales and Marketing Analytics Implementation for Verisure

Sales and Marketing Analytics Implementation for Verisure

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369

Cover image for Dashboard & Report Creation - Telco

Dashboard & Report Creation - Telco

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33

AI • Data engineering • Backend

5.0: Rating

5: Followers

AI • Data engineering • Backend

Cover image for Big Data Aggregation and Processing System

Big Data Aggregation and Processing System

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30

Cover image for AI Assistant Using LLM Agent

AI Assistant Using LLM Agent

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47

Cover image for Dynamic NextJS SaaS App

Dynamic NextJS SaaS App

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9

Cover image for High-frequency Crypto Arbitrage System

High-frequency Crypto Arbitrage System

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15

Hariharasudhan T

Mill Creek, USA

Full stack developer, AWS, GCP, Real time analytics.

Full stack developer, AWS, GCP, Real time analytics.

Cover image for USDA Health Score Prediction Using Deep Learning

USDA Health Score Prediction Using Deep Learning

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13

Cover image for Swift Soft Jobs Project

Swift Soft Jobs Project

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6

Cover image for Hariharasudhan's Personal Portfolio Website

Hariharasudhan's Personal Portfolio Website

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11

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AI Engineer | Agentic Systems & Automation | MLOps

New to Contra

AI Engineer | Agentic Systems & Automation | MLOps

Cover image for I engineered a large-scale package-network

I engineered a large-scale package-network intelligence pipeline using Azure Databricks and PySpark. The system joined approximately 4.5 billion historical records and transformed them into reusable fingerprint signals covering package movement and network behavior. I developed time-windowed feature logic using native Spark expressions rather than Python UDFs, preserving distributed performance at scale. The pipeline produced an approximately 200-million-row fingerprint dataset and a 22-million-row operational output that classified package activity along a cold-to-hot temperature scale. The resulting signals supported downstream monitoring, prioritization, and operational analysis. The architecture shown here is a sanitized representation; client identifiers, proprietary schemas, infrastructure paths, and business rules have been removed.

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Cover image for Last week I shipped a

Last week I shipped a full learning platform: two audience journeys (students + tutors), subscriptions and digital-product sales, lead capture, blog — built solo, end to end.

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Cover image for I designed and engineered a

I designed and engineered a hierarchical multi-agent orchestration framework for coordinating complex AI-assisted engineering and analytical work. A central orchestrator assigns bounded tasks to specialized builder, analyst, reviewer, and verification agents. Execution follows an explicit dependency graph, while shared evidence, acceptance gates, negative-path testing, and checkpointed state prevent agents from declaring work complete without proof. The framework supports targeted retries, rollback, and recovery from the first incomplete node instead of restarting an entire workflow. It also separates implementation from independent review, reducing the risk of an agent validating its own unsupported conclusions. This independent engineering project demonstrates how agentic systems can be made more controlled, observable, and recoverable. The architecture and control interface are representative views of the framework rather than screenshots containing private execution data.

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80

Cover image for I developed an AI-assisted document-intelligence

I developed an AI-assisted document-intelligence workflow for transportation-agency teams working with large collections of reports, standards, studies, scanned documents, and project records. The system combined document extraction, OCR, structured metadata, semantic retrieval, knowledge relationships, and grounded LLM responses. Analysts could ask questions in natural language, review the supporting evidence, inspect source citations, and approve, revise, or reject generated findings. The workflow was designed around human review, access controls, traceability, and safe failure rather than treating an LLM response as an unquestionable answer. Related work reduced decision time by approximately 60% and improved data accuracy by approximately 35%. The displayed architecture and interface are sanitized representations and contain no client documents, production data, proprietary prompts, or confidential system details.

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138

ADEWUNMI OLUWASEYI

Expert in building an efficient and scalable data pipeline

Expert in building an efficient and scalable data pipeline

Cover image for Scalable E-commerce Big Data Platform for BuildItAll

Scalable E-commerce Big Data Platform for BuildItAll

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5

Cover image for BeejanTech Analytics Platform Development

BeejanTech Analytics Platform Development

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4

Cover image for AWS Infrastructure Provisioning with Terraform

AWS Infrastructure Provisioning with Terraform

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3

Cover image for Offensive Card Booking Tracker for Football Club

Offensive Card Booking Tracker for Football Club

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4

Results-driven Data Engineer & Full-Stack Developer building

Results-driven Data Engineer & Full-Stack Developer building

Cover image for Real-time NFL Draft Prediction Model Deployment

Real-time NFL Draft Prediction Model Deployment

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7

Cover image for Real-Time Rental Market Analytics Application

Real-Time Rental Market Analytics Application

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8

Cover image for Real-Time Marketing Offer System

Real-Time Marketing Offer System

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6

Cover image for Portfolio

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7

Huntersville, USA

Microsoft Fabric | Power BI | SQL | Data-Driven Strategies

Microsoft Fabric | Power BI | SQL | Data-Driven Strategies

Cover image for End-to-End Microsoft Fabric Development

End-to-End Microsoft Fabric Development

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13

Cover image for Automated Financial Statements in Power BI

Automated Financial Statements in Power BI

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18

Cover image for Power BI Market Share Analysis Dashboard

Power BI Market Share Analysis Dashboard

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14

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Gitesh Deshmukh

Hyderabad, India

Built AI Agents, RAG & LLM Applications · MSc QMU of London

New to Contra

Built AI Agents, RAG & LLM Applications · MSc QMU of London

Cover image for CIFAR-10 Image Classification Model Development

CIFAR-10 Image Classification Model Development

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6

Cover image for Big-Data-Ethereum-Analysis

Big-Data-Ethereum-Analysis

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2

Cover image for GitHub - gitesh21/NLP-sentiment-analysis

GitHub - gitesh21/NLP-sentiment-analysis

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4

Cover image for Music Genre Classification Using Machine Learning Models

Music Genre Classification Using Machine Learning Models

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6