AI Engineer with less than a year in Machine Learning & Generative AI
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As a Machine Learning Engineer and Generative AI enthusiast, I possess a strong foundation in deep learning, NLP, and computer vision. My experience includes designing production-grade ML pipelines, developing hybrid retrieval systems for high-precision search, and orchestrating LangChain agent loops for scalable ML systems. I am passionate about leveraging AI to build innovative solutions and have a proven track record of improving model accuracy, reducing inference latency, and automating complex workflows.
Lovely Professional University
B.Tech · Computer Science and Engineering
August 1, 2022 – June 30, 2026
Tirumala Junior College
12th · Science
June 1, 2020 – May 31, 2022
DR K.K.R. Gowtham E.M. School
10th · Science
June 1, 2019 – May 31, 2020
Internship Studio
Machine Learning Engineering Intern
March 1, 2024 – May 1, 2024
India
Inspire R&D – AI-Powered Research Assistant
June 1, 2026 – Present
Engineered a Hybrid Retrieval pipeline combining SentenceTransformers semantic search, BM25 sparse keyword matching, and Cross-Encoder reranking for high-precision academic paper discovery across 10,000+ documents. Integrated Groq-hosted LLaMA 3.1 to auto-generate technical and layman summaries per paper, with an Innovation Engine that synthesizes research gaps into actionable new directions and a heuristic Patentability Score Estimator for novelty analysis. Built a Neo4j Knowledge Graph mapping papers, authors, citations, and concepts, and delivered full PDF ingestion via PyMuPDF all exposed through a FastAPI backend with Pinecone as the vector store.
View ProjectCompany Brain – Agentic RAG System
October 1, 2025 – Present
Built RAG pipeline with data ingestion, chunking, NLP embedding, and FAISS indexing to handle 10,000+ documents. Achieved sub-200ms retrieval latency with semantic search. Improved retrieval F1-score to 0.84 (22% above keyword baseline) using dense NLP embedding pipelines with overlap-aware chunking and data preprocessing strategies. Orchestrated LangChain agent loop with modular, unit-tested tool architecture enabling parallel document reasoning across 5 file types. Designed for scalable ML systems deployment.
View ProjectConsciousAI Journal – Intelligent Mental Health Companion
June 1, 2025 – Present
Built NLP data pipeline with journal ingestion, embedding generation, FAISS indexing, and LLM response generation. Improved response relevance by 40% (ROUGE-L: 0.67 vs. 0.48 baseline, n=15). Reduced inference pipeline latency from 7 seconds to 3 seconds (58% reduction) by caching FAISS index and batching embedding calls. Validated with A/B latency profiling. Designed ETL pipeline for sentiment analytics with data preprocessing, data cleaning, and null-entry validation powering 30-day trend dashboards.
View ProjectSmartCam - Real-Time Classroom Behaviour Classification System
January 1, 2025 – May 1, 2025
Achieved F1-score of 0.86 across 9 behaviour classes (Precision: 0.85, Recall: 0.88) using CNN Deep Learning classifier fused with MediaPipe skeletal features on 4,200 labelled frames. Deployed model at 12 FPS on CPU via TFLite quantisation no GPU required. Scalable ML Systems deployment on standard classroom hardware at zero marginal infrastructure cost. Built full data pipeline from raw capture to training-ready dataset: frame sampling, data cleaning, class-balance validation (5% tolerance), and augmentation with OOP modular design.
View ProjectOracle Data Platform 2025 Certified Foundations Associate
Oracle
April 1, 2026 – Present
SAP Certified Data Analyst – SAP Analytics Cloud
SAP
February 1, 2026 – Present
NLP with Classification and Vector Spaces
DeepLearning.AI
June 1, 2025 – Present
Exploratory Data Analysis for Machine Learning (with Honors)
IBM
August 1, 2024 – Present
Machine Learning Internship Certification
Internship Studio
May 1, 2024 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from AI-powered research assistants to mental health companions and classroom behavior classification, indicates a broad interest in applying AI to various domains. Their involvement in organizing community events (Building with AI) and participating in competitions suggests a proactive and collaborative mindset. The target role of AI Engineer aligns well with their demonstrated technical skills and project focus, particularly in Generative AI and Machine Learning. However, the lack of team-based professional projects beyond a single internship limits the assessment of their collaborative cultural fit in a senior capacity.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and problem-solving skills through their diverse personal projects and competition participation. Their ability to lead technical workshops suggests good communication and leadership potential. The focus on performance metrics (latency reduction, F1-score improvement) indicates a results-oriented approach. However, with limited professional experience, their operational fit in a senior role requiring extensive collaboration, mentorship, and strategic decision-making is yet to be fully validated.