AI Engineer with less than a year in LLMs & Multimodal AI
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Highly motivated and results-oriented AI/ML and Generative AI Engineer with 11 months of experience in designing and developing end-to-end multimodal AI pipelines. Proficient in LLMs, RAG, fine-tuning, and workflow orchestration, with a strong background in Python, PyTorch, and TensorFlow. Adept at optimizing backend services, building agentic systems, and implementing MLOps-driven solutions to enhance system scalability and reliability.
Indian Institute of Information Technology, Design and Manufacturing, Kurnool
B.Tech · Artificial Intelligence and Data Science
August 1, 2022 – June 30, 2026
Timepilot
ML & GenAI Engineer Intern
February 1, 2026 – Present
India
IITM Pravartak Foundation
AI Project Intern
April 1, 2025 – July 31, 2025
Chennai, Tamil Nadu, India
NIT Warangal
Research Summer Intern
May 1, 2024 – June 30, 2024
India
AI Persona Agent - Voice Agent & RAG Chat with Live Calendar Booking
January 1, 2024 – June 30, 2026
Developed a voice agent that answers questions about the candidate's background, projects, and experience using Vapi, Deepgram STT, and GPT-4o, achieving ~1.15s first-response latency. Built a two-source RAG pipeline over resume sections and GitHub READMEs using ChromaDB and Groq LLaMA-3.3-70B, validated through an LLMops evaluation framework achieving 0% hallucination rate on a 15-question golden eval set. Deployed end-to-end on HuggingFace Spaces with a Streamlit chat interface and Cal.com integration for real-time slot booking and automated confirmation emails, achieving 8/8 booking completions with no human in the loop.
View ProjectMALLM: Malware Analysis with Large Language Models
January 1, 2024 – June 30, 2026
Architected a two-stage malware analysis pipeline for systematic detection and family-wise classification using behavioral analysis of CAPEv2 sandbox execution reports. Fine-tuned open-source LLMs using LoRA-based PEFT and combined LLM-generated behavioral embeddings with ML classifiers including XGBoost and Random Forest for semantic malware analysis. Achieved 97.85% malware detection accuracy and 97.13% family classification accuracy, with findings published at ICISCN 2026.
View ProjectTriplet-Enhanced RAG for Cross-Lingual QA System
January 1, 2024 – June 30, 2026
Engineered a triplet-enhanced cross-lingual RAG pipeline for multilingual question answering across Telugu, Bengali, Arabic, and Korean using the XOR-TyDi QA benchmark. Implemented a hybrid retrieval architecture combining BM25 sparse search, CrossEncoder reranking, and REBEL-based triplet extraction with query-aware semantic ranking for structured NLP context generation. Conducted ablation studies across four RAG architectures - Sparse, Reranked, Triplet, and Triplet-Enhanced, achieving up to +12.61 F1 improvement over multilingual QA baselines.
View ProjectCultural Fit Analysis
The candidate's academic background in AI and Data Science, coupled with multiple internships and research publications, aligns well with a culture that values continuous learning, innovation, and technical excellence. Their involvement in diverse projects, including those with real-world applications (e.g., SEC filings processing, calendar booking), suggests a practical and results-oriented mindset. The achievements, such as gold medals and GATE ranking, indicate a high-achieving individual who can thrive in a challenging and competitive environment.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and a proactive approach to learning and applying advanced AI/ML concepts. Their project descriptions highlight a methodical approach to problem-solving, including evaluation frameworks and ablation studies. The ability to work on diverse projects, from voice agents to malware analysis, suggests adaptability and a strong work ethic. Experience with Git-based collaboration and MLOps practices indicates readiness for team environments and production-grade systems.