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AI Engineer with 1+ years in Cloud Operations, Machine Learning & Generative AI
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Cloud Operations & Incident Analytics Associate with experience in monitoring enterprise systems, incident management, and operational analytics. Built hands-on Machine Learning and RAG-based applications using Python, Flask, LangChain, and Azure OpenAI to explore intelligent automation and decision support. Interested in opportunities across AI/ML and Generative AI.
Malla Reddy Institute of Technology and Science, Hyderabad
Bachelor of Technology (B.Tech) · Information Technology
N/A – Present
Cognizant Technology Solutions
Cloud Operations & Incident Analytics Associate
December 1, 2024 – Present
Hyderābād, Telangana, India
Incident Resolution Copilot
June 26, 2026 – Present
Built a full-stack RAG-powered AI copilot that semantically searches 5,000 historical IT incidents and auto-generates resolution guidance - replicating the manual knowledge search performed during live P1 war-rooms. Engineered a multi-layer AI pipeline: Flask UI → FastAPI (3 REST routes) → LangChain RAG chain → Sentence Transformers (all-MiniLM-L6-v2) embeddings → FAISS vector index → Azure OpenAI (gpt-35-turbo) - each layer independently deployable. Knowledge base combines incident records, resolution notes, and 8 ITSM-specific SOPs - LLM responses grounded in real operational history, returning structured output: root cause, immediate actions, escalation team, and prevention steps. Deployed as a dual-server application (Flask on port 5000, FastAPI on port 8000) with a live copilot dashboard; full codebase available on GitHub with README and setup guide.
View ProjectTicket Priority Classifier
June 26, 2026 – Present
Built a complete ML pipeline - synthetic dataset generation, feature engineering, model training, and a Flask web app to classify ITSM incidents as P1-P4 with 91% test accuracy. Engineered domain-specific features (escalation count, environment criticality, SLA breach risk, repeat incident flags) that generic ML approaches miss grounded in 1-2 years of live incident operations. Applied and compared Logistic Regression, Random Forest, and XGBoost; tuned hyperparameters and validated with hold-out test set. Deployed as a local Flask app with a web UI full codebase available on GitHub with README, EDA notebook, and model evaluation results.
View ProjectIntroduction to Python Programming
Infosys Springboard, Infosys Ltd.
June 1, 2026 – Present
AI-900: Microsoft Azure AI Fundamentals
Microsoft
June 1, 2026 – Present
Complete Data Science, Machine Learning & Deep Learning Bootcamp
Udemy
June 1, 2026 – Present
Artificial Intelligence & Machine Learning Bootcamp
Apna College
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, focusing on both traditional ML classification and advanced RAG systems, shows a broad interest within AI/ML. Their current role as a Cloud Operations & Incident Analytics Associate, combined with their personal AI projects, aligns well with an AI Engineer role that might involve applying AI to operational challenges. The breadth of skills listed, from core programming to various ML/DL techniques and Generative AI, indicates a versatile and adaptable individual. The candidate's engagement in certifications and online competitions further suggests a proactive learning mindset, which is a strong cultural fit for dynamic tech environments.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking and problem-solving skills through their project work, particularly in engineering domain-specific features and designing complex AI pipelines. Their experience in coordinating P1/P2 incident response and contributing to post-incident reviews suggests an ability to work under pressure and contribute to continuous improvement. The project descriptions indicate good communication of technical details and architectural choices. The candidate's initiative in building personal projects inspired by real operational workflows highlights a proactive and practical approach to problem-solving.