AI Engineer with 1+ years in Machine Learning & Cloud Infrastructure
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Highly analytical and detail-oriented B.Tech AI/ML student actively seeking an entry-level Data Scientist or Machine Learning Engineer position. Possesses hands-on experience building reproducible end-to-end predictive pipelines, statistical modeling applications, and containerized AI solutions. Passionate about uncovering actionable business insights from unstructured data, automating model tracking, and scaling algorithmic decisions to solve complex organizational challenges.
Sri Indu Institute of Engineering and Technology
Bachelor of Technology (B.Tech) · Artificial Intelligence & Machine Learning
August 1, 2022 – June 30, 2026
Vagdevi Junior College
Intermediate Education (MPC)
N/A – May 31, 2022
Brilliant Grammar High School
Secondary Education (Class X)
N/A – May 31, 2020
Shnoor International LLC
Software Intern
April 1, 2026 – Present
India
Infosys Springboard
AI/ML Intern
July 1, 2025 – September 1, 2025
Hyderābād, Telangana, India
Research Navigator: Semantic Paper Analyzer
June 24, 2026 – Present
Designed and developed an intelligent research paper analysis platform enabling semantic search, summarization, and contextual question answering over academic documents. Leveraged Hugging Face Sentence Transformers to generate vector embeddings, reducing literature review lookup time by 60.
View ProjectShnoor Meetings: AI-Enabled Video Conferencing Platform
June 24, 2026 – Present
Developed a full-stack Google Meet-style video conferencing platform utilizing WebRTC peer-to-peer communication and WebSockets for low-latency real-time signaling, room management, and live chat. Integrated Groq Whisper-Large-v3 API for high-accuracy speech-to-text transcription and live caption generation during meetings. Engineered a post-meeting AI analyzer using DeepSeek-R1-Distill (OpenRouter) to generate contextual summaries, searchable meeting logs, and action items from transcribed conversations.
View ProjectMultiDocChat: Cloud-Native Conversational RAG Portal
June 24, 2026 – Present
Engineered a production-grade conversational RAG pipeline using FastAPI and FAISS vector databases, resolving context dilution and reducing 35. Designed a decoupled model runtime engine using dynamic provider abstraction to hot-swap between DeepSeek, Gemini, and Groq APIs, reducing code maintenance overhead by 40. Architected secure cloud infrastructure on AWS ECS Fargate using AWS Secrets Manager, GitHub Actions, and multi-stage Docker builds, reducing image size by 55.
View ProjectStudent Performance Prediction Pipeline with MLflow and Dagshub Tracking
June 24, 2026 – Present
Built an end-to-end Machine Learning pipeline using Python and SQL for data ingestion, data cleaning, EDA, feature engineering, and regression modeling, achieving a test R2 score of 0.88. Trained and evaluated multiple regression algorithms including Linear Regression, Random Forest, AdaBoost, XGBoost, and CatBoost using GridSearchCV and cross-validation. Performed statistical analysis, feature selection, outlier handling, and model evaluation using RMSE, MAE, and R2 metrics to improve predictive performance. Integrated MLflow and Dagshub for experiment tracking, model versioning, artifact management, and reproducible MLOps workflows supporting future deployment.
View ProjectMachine Learning Specialization
DeepLearning.AI & Stanford
June 1, 2026 – Present
Artificial Intelligence Certificate
Infosys Springboard
June 1, 2026 – Present
Gen AI Exchange Program Certificate
Hack2Skill
June 1, 2026 – Present
Practical AI/ML Certificate
Systemtron Technologies
June 1, 2026 – Present
Academic Excellence Award
Unknown
June 1, 2026 – Present
Credential Verification
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse personal projects, ranging from semantic paper analyzers to AI-enabled video conferencing and RAG portals, showcase a strong passion for AI/ML and a proactive approach to learning and application. Their engagement in an AI/ML self-learning and community journey, including peer mentoring and open-source contributions, indicates a collaborative spirit and a desire to share knowledge, which aligns well with a culture of continuous improvement and teamwork. The breadth of skills across full-stack development, MLOps, and various AI/ML paradigms suggests adaptability and a willingness to tackle different challenges.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, adaptability, and a clear understanding of team collaboration, as highlighted in their technical skills and project descriptions. Their involvement in systematic debugging and code reviews during internships indicates a good operational fit for development teams. The self-learning and community journey also points to proactivity and a collaborative mindset.