Karnekota Rishi Pavan
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with less than a year in Data Science & Machine Learning
Data Science graduate with expertise across analytics, engineering, and machine learning. Proficient in Python, SQL, and Power BI for analysis, visualization, and reporting. Skilled in ETL pipelines, BI dashboards, and deploying scalable cloud solutions. Experienced in ML workflows (scikit-learn, TensorFlow/Keras) and Generative AI projects like RAG chatbots with vector databases. Certified in cloud and analytics foundations, with practical exposure through Deloitte's Data Analytics simulation. Strong communicator adept at turning complex data into actionable insights, ready to contribute as Data Analyst, Data Engineer, or AI/ML Engineer.
Great Lakes Institute of Management
PG Program in Data Science (GenAI) · Data Science (GenAI)
August 1, 2026 – June 30, 2026
GITAM University, Hyderabad
B.Tech · Computer Science Engineering
August 1, 2024 – June 30, 2024
SYNTECXHUB
Data Science Intern
April 1, 2026 – May 1, 2026
India
SIG Overseas
Client Relationship Officer Intern
January 1, 2024 – June 1, 2024
Hyderābād, Telangana, India
CuraLink - AI Medical Research Assistant (Hackathon, Humanity Founders)
June 1, 2026 – June 1, 2026
Engineered a zero-latency in-memory pipeline reducing query times from 4 minutes to ~10s, ensuring source-backed, hallucination-free medical insights. Built full-stack app with React, Node.js, MongoDB, and Llama-3, integrating PubMed/OpenAlex/ClinicalTrials APIs for real-time research assistance.
Context-Aware LLM Chatbot (RAG Pipeline)
January 1, 2026 – June 1, 2026
Developed a RAG-based chatbot with data ingestion pipelines and visualization of query logs. Integrated vector databases for domain-specific responses.
Customer Churn Prediction with Machine Learning (ML Pipeline)
January 1, 2025 – December 31, 2025
Built Random Forest and XGBoost models on a 10,000-customer dataset, achieving 87% accuracy in identifying at-risk customers. Applied full ML pipeline - data cleaning, EDA, feature engineering, and hyperparameter tuning to address class imbalance and deliver actionable retention insights.
Python, SQL, Machine Learning
Coursera
June 1, 2026 – Present
AWS Academy Cloud Foundations
AWS Academy
June 1, 2026 – Present
AWS Academy Machine Learning Foundations
AWS Academy
June 1, 2026 – Present
Deloitte Data Analytics Job Simulation
Deloitte
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background, diverse project portfolio (academic, personal, hackathon), and certifications indicate a proactive and continuous learning mindset, which aligns well with a dynamic AI engineering environment. The focus on Generative AI and ML, coupled with practical application in projects like the RAG chatbot and medical research assistant, suggests a strong interest and alignment with cutting-edge AI development. The hackathon experience also points to a collaborative and results-oriented approach.
Soft Skills & Operational Fit
The candidate's profile highlights strong communication skills, adept at turning complex data into actionable insights. Project descriptions suggest an ability to work on diverse tasks, from data ingestion to model deployment and full-stack application development. The hackathon project demonstrates problem-solving under pressure and an ability to deliver impactful solutions quickly. The internship experience, though limited, shows exposure to data collection, preprocessing, and pipeline optimization, indicating a foundational understanding of operational workflows.