// INIT_SEQUENCE
AI/ML Engineer building intelligent systems at the intersection of language models, agents, and real-world automation.
$ whoami
AI/ML Engineer · Bengaluru, KA
$ cat skills.txt | grep AI
Agents · RAG · Fine-tuning · Computer Vision
$ uptime
May 2025 - Present @ Gloroots|
// NODE_01
I'm an AI/ML Engineer at Gloroots, where I build specialized AI agents and internal tools that reshape how recruitment pipelines operate - from automated interview assessments to sales-efficiency workflows.
I studied B.Tech in Artificial Intelligence and Robotics at Madhav Institute of Technology and Science (CGPA 8.27/10), which gave me a solid grounding in both the hardware intuition of robotics and the software depth of modern AI.
My work spans fine-tuning language models, graph-based retrieval systems, generative AI pipelines, and real-time security applications. I care about systems that are fast, purposeful, and actually deployed - not just notebooks.
EDUCATION
Madhav Institute of Technology & Science
B.Tech · AI & Robotics · Aug 2021 - May 2025
CGPA: 8.27 / 10
ACHIEVEMENTS
1st Prize - MP Young Scientist Congress (Feb 2024) · Ayurvedic medicine recommender
1st Runner-Up - Hack The Mountain 4.0 (Oct 2023) · Blockchain electricity trading platform
// NODE_02
AI/ML Engineer
Gloroots
AI/ML Intern
Gloroots (Recrew.ai)
AI Intern
Galleri5 - Collective Artists Network
// NODE_03
Feb 2024
RAG-based Ayurvedic medicine recommender using Gemini - won 1st Prize at MP Young Scientist Congress 2024.
Apr 2024
Real-time security system bridging hardware (Arduino pressure plate) with Gemini Vision to detect suspicious activity.
Sep 2024
Graph-based knowledge system integrating Neo4j and LangChain for context-aware medical Q&A with efficient retrieval.
Apr 2025
Multi-agent LangGraph framework where specialized agents collaboratively perform market research and ideate AI use cases with resource links.
Audio-based AI interview platform for conducting and evaluating technical interviews remotely.
Aug 2025
Designed an arena to benchmark LLM performance in adversarial UNO - testing reasoning, bluffing, and strategy.
Fine-tuned Llama 3.2 3B using PEFT (LoRA, gradient checkpointing) and Unsloth on 'The Tome' dataset, enabling effective multi-turn conversations.
// NODE_04
LANGUAGES
AI / ML
CLOUD / DB
WEB / FRAMEWORKS
// NODE_05
I'm open to interesting problems, freelance work, and full-time roles that involve building with LLMs, agents, or AI pipelines. Drop a message - I read everything.
CURRENT_STATUS
role: AI/ML Engineer
loc: Bengaluru, KA
open: Full-time · Contract · Research
resp: < 48h