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Software EngineerPHONE NUMBER AVAILABLE | EMAIL AVAILABLE | New York City, NYProfessional SummaryInnovative Software Engineer with 3+ years of experience designing and implementing scalable AI solutions across AWS, Azure, and GCP. Expertise in deep learning, natural language processing, and generative AI, with a strong focus on creating business value through cutting-edge technology. Proven track record of optimizing cloud infrastructure for ML workloads and driving adoption of best practices in MLOps and DataOps.Core Competencies      Machine Learning & Deep Learning      Cloud Architecture (AWS, Azure, GCP)      Natural Language Processing & Computer Vision      MLOps & DataOps      Generative AI & Large Language ModelsProfessional Experience Software Engineer7-Eleven | New York, NY | Feb 2024 - Present-Lead the development and implementation of advanced ML and GenAI solutions to drive business innovation and operational efficiency in the retail sector.-Architected and deployed a generative AI system using Java to integrate GPT-4 APIs and custom Java-based microservices for product description generation and visual content creation with DALL-E 3. This reduced time-to-market for new products by 40%.-Implemented a Retrieval-Augmented Generation (RAG) system for customer service chatbots using Java to manage retrieval processes and orchestration logic. Improved first-contact resolution rates by 35% and cut average handling time by 25%.-Designed and deployed a comprehensive data catalog and governance framework using Java in conjunction with Collibra APIs, increasing data discovery efficiency by 50% while ensuring compliance with data protection regulations.-Led the adoption of MLOps best practices by integrating Java-based CI/CD pipelines using GitHub Actions and Java Spring Boot with ArgoCD for deployment, reducing model deployment time from weeks to hours.Software EngineerCaterpillar Inc. | Remote, USA | Aug 2023 - Jan 2024-Spearheaded the development of ML solutions on cloud platforms to optimize heavy machinery operations and predictive maintenance.-Developed and deployed a real-time anomaly detection system using TensorFlow on AWS SageMaker, reducing unplanned downtime of heavy machinery by 30% and saving an estimated $10M annually.- Implemented a computer vision solution using AWS Rekognition and custom PyTorch models to automate quality control in manufacturing, improving defect detection accuracy by 45%.- Architected a multi-cloud ML pipeline using AWS Step Functions and Azure ML, enabling seamless model training and deployment across cloud platforms, reducing infrastructure costs by 25%. Pioneered the use of Kubernetes for orchestrating ML workloads, improving resource utilization by 40% and enabling effortless scaling of ML services.Software EngineerKPMG | Chennai, India | Jan 2021 - August 2022-Contributed to the development of data-driven solutions for financial services clients, focusing on fraud detection and risk assessment.-Developed a fraud detection model using Random Forests and XGBoost, identifying potential fraud cases with 88% accuracy and saving the client an estimated $5M annually.- Implemented an NLP-based system for analyzing financial documents using spaCy and TensorFlow, reducing manual review time by 60%.- Created interactive dashboards using Tableau and PowerBI, providing clients with real-time insights into key financial metrics and risk indicators.-Pioneered the use of Kubernetes for orchestrating ML workloads, improving resource utilization by 40% and enabling effortless scaling of ML services.Education- M.S. in Computer Science, Specialization in AI & ML | Stevens Institute of Technology, Hoboken | 2023- B.E. in Electronics and Communication Engineering | Anna University, Chennai | 2022Certifications- AWS Certified Machine Learning - Specialty- Microsoft Certified: Azure AI Engineer Associate- Google Cloud Professional Machine Learning Engineer- Databricks Certified Associate Developer for Apache SparkTechnical Skills- Languages: Python, SQL, R, Scala- ML/DL Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, Hugging Face Transformers- Cloud Platforms: AWS (SageMaker, Lambda, EC2, S3), Azure (Azure ML, Databricks), GCP (Vertex AI, BigQuery)- Big Data: Apache Spark, Hadoop, Kafka- Containerization & Orchestration: Docker, Kubernetes- MLOps Tools: MLflow, Kubeflow, Airflow, GitHub Actions- Data Visualization: Tableau, PowerBI, Matplotlib, Seaborn- Databases: PostgreSQL, MongoDB, CassandraPublications & Speaking Engagements- "Leveraging Generative AI in Retail: A Case Study" - AI Conference, New York, 2024- "Scaling ML Pipelines Across Multi-Cloud Environments" - AWS re:Invent, Las Vegas, 2023