MLOps Engineer with 2+ years in LLM Deployment & MLOps
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 & Data Science engineer and M.Tech (Defence Technology) candidate with hands-on experience deploying production-grade AI systems for defence and enterprise environments. Skilled in Python, LLM deployment, MLOps, full-stack development, and offline-first AI architecture. Experienced in containerising LLMs, embedding models, and OCR systems using Docker, vLLM, and Hugging Face TEI on enterprise GPU hardware.
Amity University
M.Tech · Defence Technology
N/A – June 30, 2026
Misrimal Navajee Munoth Jain Engineering College
B.Tech · Artificial Intelligence & Data Science
N/A – June 30, 2023
L&T Precision Engineering Systems
AI Intern
December 1, 2025 – Present
India
Wardec (Wargaming Development Centre)
Software Development Intern
August 1, 2025 – December 1, 2025
New Delhi, Delhi, India
Novi Tech R&D Private Limited
Artificial Intelligence Intern
April 1, 2024 – May 1, 2024
India
Deepfake Detection Using Vision Transformers
June 18, 2026 – Present
• Transformer-based deepfake detection using ViT attention mechanisms to differentiate authentic from synthetic media.
Customer Churn Prediction
June 18, 2026 – Present
• ANN churn prediction model (TensorFlow/Keras)
Stroke Prediction System
June 18, 2026 – Present
• multi-algorithm stroke risk ML system (Scikit-learn).
AI Model Deployment & MLOps Infrastructure
June 18, 2026 – Present
• Containerised 3 production-grade AI models as self-contained Docker images on NVIDIA RTX A6000 (48GB): Gemma 4 E4B (LLM, 128k context), GLM-OCR 0.9B (document OCR), Qwen3-Embedding-0.6B (semantic embeddings). • Implemented offline-first deployment — model weights baked into Docker images eliminating runtime internet dependency; published to Docker Hub. • Configured NVIDIA Container Toolkit for GPU passthrough; resolved corporate network restrictions via host-side model download and COPY-based image baking.
View ProjectAI-Powered Eisenhower Matrix Task Manager
June 18, 2026 – Present
• LLM-based task classifier (Ollama)
Python Technologies
Neon Education Centre
June 1, 2026 – Present
Deepfake Detection Using Vision Transformers
IJSART
May 1, 2024 – Present
Cultural Fit Analysis
The candidate's project portfolio is diverse, spanning academic research, personal projects, and professional internships, all with a strong focus on AI and MLOps. The experience in defence-related projects and secure environments aligns well with roles requiring high reliability and security. The breadth of skills, from low-level system programming (Swift, Objective-C++, C++) to high-level AI frameworks and full-stack development, indicates adaptability and a willingness to tackle varied technical challenges. The pursuit of an M.Tech in Defence Technology further reinforces a strong alignment with roles that might involve secure or specialized AI deployments.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude, particularly in overcoming complex deployment challenges like corporate network restrictions and building zero-external-dependency components. The focus on 'offline-first' and 'intranet-based' solutions suggests a pragmatic and security-conscious approach, which is critical for MLOps roles in sensitive environments. The detailed descriptions of engineering pipelines and system configurations demonstrate a methodical and thorough operational mindset.