Tech professional with a strong 𝗯𝗶𝗮𝘀 𝗳𝗼𝗿 𝗮𝗰𝘁𝗶𝗼𝗻 and a proven track record in Generative AI, backend development, cloud infrastructure, automation, and data-driven solutions. Thriving in fast-paced startup environments, I bring expertise in Python, AWS, DevOps, CI/CD, and containerization (Docker/Kubernetes) to drive technological advancements and operational efficiency.𝗖𝗼𝗿𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝗶𝗲𝘀:𝘗𝘳𝘰𝘨𝘳𝘢𝘮𝘮𝘪𝘯𝘨 𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦𝘴: Python, SQL, NoSQL, Bash, Go (Golang), Mojo, HTML, CSS, JavaScript𝘋𝘢𝘵𝘢𝘣𝘢𝘴𝘦𝘴: MySQL, PostgreSQL, SQLite, ClickHouse, DuckDB, Elasticsearch, OpenSearch, Redis, MongoDB𝘓𝘪𝘣𝘳𝘢𝘳𝘪𝘦𝘴 & 𝘍𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬𝘴: FastAPI, LangChain, LlamaIndex, NumPy, Pandas, Polars, Scikit-learn, OpenCV, Pillow, Tesseract, Matplotlib, Boto3, Selenium𝘊𝘭𝘰𝘶𝘥 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘪𝘦𝘴: AWS, Docker, Terraform, Kubernetes𝘋𝘦𝘷𝘖𝘱𝘴 & 𝘊𝘐/𝘊𝘋 𝘛𝘰𝘰𝘭𝘴: GitLab, GitHub, AWS CodePipeline, Kafka𝗞𝗲𝘆 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:• Enabled asynchronous multi-LLM architecture with streaming output, concurrent task completion, and implemented telemetry for LLM output tracing and pricing.• Successfully deployed a SaaS application using Docker, Terraform, and Kubernetes on Amazon EKS, ensuring scalability and maintainability.• Received Employee of the Month awards, recognizing consistent high performance and valuable contributions to the team.• Led cloud migration projects, optimizing AWS infrastructure to achieve over 50% cost reduction, significantly improving operational efficiency.• Architected real-time data synchronization solutions, drastically improving query performance from minutes to seconds.• Implemented robust CI/CD pipelines and DevOps practices, enhancing deployment efficiency and code quality.• Developed automated systems for question paper generation and OMR processing using Python and computer vision libraries, streamlining educational assessment processes.𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗙𝗼𝗰𝘂𝘀:Advancing AI-driven backend solutions at a generative AI company, working on:• Integrating LLM frameworks, vector databases, and LLM models with Python backends for SaaS products• Implementing Retrieval-Augmented Generation (RAG) systems for more accurate and contextual AI responses• Utilizing AWS, Docker, Terraform, and Kubernetes to build and scale microservices and SaaS applications• Developing telemetry and tracing systems for pricing models and log management• Managing AWS infrastructure to optimize performance and reliability