About Me
AI/ML Data Engineer with 3+ years of experience in designing, developing, and deploying machine learning solutions for business applications. Proficient in Python, SQL, Scikit-learn, TensorFlow, and PyTorch for developin…
AI/ML Data Engineer with 3+ years of experience in designing, developing, and deploying machine learning solutions for business applications. Proficient in Python, SQL, Scikit-learn, TensorFlow, and PyTorch for developing, training, testing, and deploying ML models. Experienced in data preprocessing, feature engineering, and model development with structured and unstructured data. Demonstrated expertise in Generative AI, LLMs, NLP, and RAG-based applications including prompt engineering and vector databases. Skilled in cloud platforms (AWS), REST APIs, microservices architecture, and CI/CD pipelines. Strong communicator collaborating effectively with cross-functional teams, data engineers, developers, and business stakeholders to deliver AI-driven solutions.
Experience
AI Software Engineer
<p>Developed, trained, tested, and deployed machine learning models for business applications, building scalable data pipelines processing 5M+ records daily for AI/ML products, reducing manual data preparation by 60%</p><p> Implemented AI/ML algorithms using Python and relevant frameworks including Scikit-learn, TensorFlow, and PyTorch, with comprehensive data quality validation and monitoring ensuring downstream models received trusted inputs</p><p> Worked with structured and unstructured data for data preprocessing, feature engineering, and model development, structuring data for RAG pipelines using Pinecone and Chroma vector databases</p><p> Collaborated with data engineers, developers, and business stakeholders to deliver AI-driven solutions, productionizing data flows for agentic systems with 4 production microservices handling 10K+ daily requests with 99.9% uptime</p><p> Prepared technical documentation and presented findings to technical and non-technical audiences, contributing to design discussions and code reviews using GitHub Actions and CI/CD</p><p> Optimized model performance through PostgreSQL and MongoDB query optimization, improving API response from 800ms to 210ms while supporting 3x traffic growth</p><p> Stayed updated with latest advancements in AI, ML, and Generative AI technologies, implementing prompt engineering and hallucination control mechanisms</p>
PROJECTS
Intelligent Analytics Platform – BIR
<ul><li>Engineered ETL systems processing 2M+ racing records for real-time feature engineering and predictive analytics using Python and AWS Lambda Developed ML models with LangChain-powered agents and OpenAI embeddings, implementing semantic search and reducing inference latency by 45% through optimized orchestration </li><li>Implemented vector database-backed search using Pinecone for dynamic calculations and trend analysis, enabling intelligent product recommendations</li><li> Deployed on AWS Lambda and API Gateway with auto-scaling capabilities, achieving 99.8% uptime during peak traffic seasons</li></ul>
Gaming Platform – Champions
<ul><li>Built data pipelines using Python and NLP libraries with secure validations and debugging workflows for regulated environment compliance </li><li>Improved platform reliability by 25% through automated log analysis and real-time anomaly detection in game sessions </li><li>Integrated fine-tuned BERT model for intent classification, boosting accuracy from 78% to 93% through model optimization </li><li>Reduced user-reported issues by 30% with automated debugging pipelines and session replay analytics</li></ul>
E-Commerce Platform – KSIC
<ul><li>Built data pipelines for intelligent platform handling 500+ daily transactions with JWT authentication and intelligent product search </li><li>Implemented recommendation system using OpenAI embeddings and collaborative filtering, improving conversion rates by 15% through evidence-based iteration </li><li>Designed vector-based product similarity engine in Pinecone, reducing search response time from 900ms to 180ms </li><li>Deployed microservices on AWS EC2 with auto-scaling and Redis caching to handle flash sale traffic spikes ensuring production reliability</li></ul>
UpSkhill Educational Platform
<ul><li>Developed scalable microservices onboarding 8K+ learners with semantic search using vector databases and OpenAI APIs for enhanced information retrieval </li><li>Designed feature stores and data pipelines for personalized content recommendation, increasing average session duration by 30% </li><li>Integrated RAG-based Q&A systems answering learner queries instantly, reducing support tickets by 40% through effective document processing</li><li>Containerized services with Docker and automated CI/CD pipelines via GitHub Actions for zero-downtime deployments</li></ul>