Freelancers using Azure in KarnatakaFreelancers using Azure in Karnataka
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Sowmya Lanka's avatar
Sowmya Lanka
Bengaluru, India
Data Scientist | ML, NLP & GenAI Solutions
New to Contra
Data Scientist | ML, NLP & GenAI Solutions
Cover image for AI-Powered Policy Q&A Assistant Built a
AI-Powered Policy Q&A Assistant Built a Retrieval-Augmented Generation (RAG) chatbot using Azure OpenAI GPT to answer policy-related questions with accurate, context-aware responses. What I built Extracted and structured information from policy documents into a knowledge base. Used Azure Cosmos DB for scalable information storage and retrieval. Implemented RAG to retrieve relevant policy information based on user queries. Integrated Azure OpenAI GPT to generate natural-language, context-aware answers. Technologies Python · Azure OpenAI · GPT · RAG · Azure Cosmos DB · LangChain Outcome The solution enables users to quickly find relevant information from policy documents without manually searching through lengthy documents.
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Cover image for What I built Developed a machine
What I built Developed a machine learning solution to predict Buy Box and sales prices using historical analytical data and Linear Regression. How it works Analyzed the data for linear relationships, autocorrelation, multicollinearity, homoscedasticity, and normally distributed errors. Applied Principal Component Analysis (PCA) for dimensionality reduction and to improve model performance. Identified key business factors influencing price prediction. Evaluated the model using R², Adjusted R², RMSE, MAE, and MSE metrics. Technologies Python · Pandas · NumPy · Scikit-learn · Linear Regression · PCA Outcome Built a price prediction model that identified important business drivers and evaluated prediction performance using multiple regression metrics.
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Cover image for What I built Developed a Natural
What I built Developed a Natural Language Processing (NLP) solution to detect DI Flag issues by analyzing and classifying text descriptions. How it works Cleaned text data by removing hashtags, HTML tags, special characters, and numeric values. Applied text preprocessing techniques including tokenization, stop-word removal, stemming, and lemmatization. Converted text into numerical features using Bag of Words (BoW) and TF-IDF. Trained and evaluated the model using accuracy and confusion matrix metrics. Performed 30 days of validation testing before production deployment. Technologies Python · NLP · Scikit-learn · TF-IDF · Bag of Words · Pandas · NumPy Outcome Built a text classification solution that helped identify DI Flag issues from descriptions and validated the model's performance before deployment.
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Cover image for What I built Developed an automated
What I built Developed an automated clinical text summarization solution using Google Gemini AI and prompt engineering to generate concise summaries from lengthy clinical chart notes, transcripts, and unstructured medical text. How it works Processed lengthy clinical notes and transcripts. Used prompt engineering to guide Gemini in identifying important medical information. Extracted key details such as symptoms, diagnoses, medications, and treatment plans. Generated concise, readable summaries from the original clinical text. Technologies Google Gemini AI · Python · Prompt Engineering · NLP · Generative AI Outcome Reduced clinical chart review time by approximately 30%, helping healthcare professionals identify important information more quickly and efficiently.::
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Azure(1)
Ishan Trivedi's avatar
Ishan Trivedi
Bengaluru, India
LLM Reliability · Evaluation · Fine-Tuning · Production AI
New to Contra
LLM Reliability · Evaluation · Fine-Tuning · Production AI
Cover image for Built a production-oriented AI platform
Built a production-oriented AI platform that accelerates engineering incident investigations using Retrieval-Augmented Generation (RAG). The system ingests engineering documentation, logs, runbooks, and deployment data to generate citation-backed answers for faster root cause analysis.
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Cover image for AudioForge — Parameter-Efficient Audio Classification Built
AudioForge — Parameter-Efficient Audio Classification Built and evaluated an audio-classification pipeline on FSD50K, covering 36K+ training clips across 200 multi-label sound categories. I compared a CNN trained from scratch against a pretrained Audio Spectrogram Transformer adapted using LoRA. The LoRA setup trained only 0.52% of the AST’s 86.8M parameters while improving mean Average Precision from 0.302 to 0.557 — an 84% improvement. Beyond model training, I handled the GPU infrastructure end-to-end: VRAM sizing, AWS EC2 provisioning, quota management, spot-instance fallback, cost-controlled start/stop workflows, and debugging failed training runs with smoke tests before full experiments. What I worked on Audio Spectrogram Transformer fine-tuning LoRA / parameter-efficient training Multi-label audio classification PyTorch training and evaluation pipelines GPU memory and infrastructure optimization Experiment debugging and validation Stack: Python · PyTorch · Hugging Face · AWS EC2
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Cover image for Autonomous AI red-team platform. Point
Autonomous AI red-team platform. Point it at any HTTP AI agent — a 5-agent LangGraph pipeline attacks it, finds vulnerabilities, proposes defenses, and streams results live to a React dashboard.
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Cover image for Fine-tuned a Qwen2.5-7B language model
Fine-tuned a Qwen2.5-7B language model using QLoRA to automatically analyze system logs and produce structured incident reports containing root cause, severity, confidence score, and recommended fixes. PyTorch • Hugging Face • PEFT • QLoRA • AWS EC2 • Transformers
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Azure(1)
Swathi 's avatar
Swathi
Bengaluru, India
SQL, MSBI, Pyton, Data Engineering & Azure Solutions Expert
SQL, MSBI, Pyton, Data Engineering & Azure Solutions Expert
Cover image for Federated Data Lake
Federated Data Lake
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Cover image for Customer Data Management
Customer Data Management
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Cover image for Data Reconciliation
Data Reconciliation
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Karan Singh's avatar
Karan Singh
Bengaluru, India
Full-Stack AI Engineer | 4X 0-​1 Builder | Go, Py, LLM & K8s
New to Contra
Full-Stack AI Engineer | 4X 0-​1 Builder | Go, Py, LLM & K8s
Cover image for Speech based AI Clinical Documentation Intelligence platform.
Speech based AI Clinical Documentation Intelligence platform.
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Cover image for whyLLM — Every LLM call, traced and controlled
whyLLM — Every LLM call, traced and controlled
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Cover image for MudraCore OS - A Runnable Fintech Operating System
MudraCore OS - A Runnable Fintech Operating System
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Cover image for Interoperable E-Wallet Architecture based on DLT & Blockchain
Interoperable E-Wallet Architecture based on DLT & Blockchain
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Azure(1)
Shubham Dixit's avatar
Shubham Dixit
Bengaluru, India
Platform Engineering & DevOps Consultant helping teams build
New to Contra
Platform Engineering & DevOps Consultant helping teams build
Cover image for I led the modernization of
I led the modernization of a large-scale cloud platform by implementing Infrastructure as Code, Kubernetes, and automated CI/CD pipelines across AWS and Azure. The initiative improved deployment reliability, reduced manual operations, and accelerated software delivery. The platform enabled engineering teams to provision infrastructure faster, deploy applications more frequently, and operate securely at scale.
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Cover image for Designed and implemented a cloud-native
Designed and implemented a cloud-native API platform with automated CI/CD, security controls, and observability built in. The platform streamlined software delivery, improved developer productivity, and established consistent engineering standards. Teams were able to release features faster while maintaining high reliability and quality. These descriptions are written in the style that typically performs well on Contra and consulting portfolios because they focus on business outcomes, scale, and impact, not just technologies.
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Cover image for Built an intelligent operations platform
Built an intelligent operations platform leveraging centralized observability, AI-driven root cause analysis, and automated remediation. The solution continuously analyzes logs, metrics, traces, and alerts to detect anomalies, identify probable causes, and execute recovery actions automatically. This significantly reduced incident resolution times and improved platform reliability.
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Cover image for Cloud Platform Modernization for a Fortune 500 Institution
Cloud Platform Modernization for a Fortune 500 Institution
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Azure(1)