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AI Engineer with less than a year in LLMs, MLOps & Python Development
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AI Engineer and Python Developer specializing in Large Language Models (LLMs), MLOps, and scalable AI infrastructure. Proven track record in orchestrating LangChain agents, automating LLM testing frameworks using Py-Torch and TensorFlow, and deploying high-performance conversational engines. Strong foundation in vector databases and RESTful API development (FastAPI, Django) to bridge complex backend data streams with advanced AI functionalities. Currently pursuing an advanced degree in Data Science to further enhance technical expertise in forecasting and machine learning architecture.
Vellore Institute of Technology (VIT)
Master of Computer Applications (MCA)
N/A – June 30, 2026
Capital University
Bachelor of Computer Applications (BCA)
N/A – June 30, 2025
VET Manandi PU College
12th Grade (PUC)
N/A – May 31, 2017
AI Central & AI Labs
AI Engineer
May 1, 2026 – Present
Bengaluru, Karnataka, India
MainCrafts Technologies
Data Science - Intern
January 1, 2026 – April 30, 2026
Bengaluru, Karnataka, India
AI-Driven Product Recommendation Engine
June 24, 2026 – Present
Designed and implemented a hybrid recommendation system for an e-commerce platform, inspired by Amazon's "Customers who bought this also bought..." feature. Built a user-item interaction matrix using Pandas and applied collaborative filtering, cosine similarity, and content-based filtering to generate personalized product suggestions. Developed and deployed a REST API endpoint (/api/recommendations/<user_id>) with Django REST Framework to deliver recommendations in JSON format for seamless frontend integration. Integrated MySQL database to manage user purchase history and product catalog, ensuring scalable and reliable data storage. Optimized performance by implementing Redis caching for pre-computed similarity matrices and recommendation lists, reducing API latency from seconds to milliseconds. Automated data ingestion from Kaggle datasets to seed product and order tables, enabling rapid prototyping and testing with real-world data.
Supermarket Sales Data Analysis
June 24, 2026 – Present
Engineered complex MySQL queries with JOINs and subqueries to extract actionable insights from large-scale sales datasets. Utilized Pandas for advanced data cleaning, filtering, and transformation, enabling efficient trend analysis and reporting. Applied indexing and query optimization techniques, reducing SQL query execution time by 30% and improving overall system performance. Optimized data extraction by writing complex MySQL queries, including JOINs and subqueries. Leveraged the Pandas library to clean, filter, and manipulate large datasets for efficient analysis. Applied indexing techniques to improve SQL query performance..
Live Chat Bot Web Application
June 24, 2026 – Present
Built a rule-based chatbot using the Flask micro-framework, delivering real-time customer interaction capabilities. Designed modular Python logic for dynamic response generation, ensuring scalability and maintainability of the application. Deployed the chatbot as a lightweight web application, enhancing user engagement and demonstrating end-to-end development skills.
Cultural Fit Analysis
The candidate's project diversity, ranging from recommendation engines to data analysis and chatbots, indicates a broad interest in AI applications. The target role of 'AI Engineer' aligns well with their professional experience at 'AI Central & AI Labs' and 'MainCrafts Technologies', where they focused on LLMs, RAG, API development, and cloud deployment. The breadth of skills, including various AI/ML frameworks, backend technologies, and cloud platforms, suggests adaptability and a willingness to learn, which are positive indicators for cultural fit in a dynamic environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project implementations like the recommendation engine and data analysis. Their experience with Jira suggests an understanding of agile methodologies and bug tracking, indicating good operational fit. The descriptions imply an ability to work in cross-functional teams, particularly during cloud deployment and project cycles.