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ML Engineer with less than a year in deep learning, MLOps, and cloud deployment.
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Computer Science undergraduate (graduating May 2026) specializing in machine learning and AI engineering, with end-to-end experience across the ML lifecycle: fine-tuning, computer vision, LLM/RAG systems, evaluation, and production deployment. Skilled in Python and PyTorch across deep learning (YOLOv8, Mistral-7B QLORA), classical ML, RAG, MLOps, and cloud deployment on AWS and GCP.
Vellore Institute of Technology, Vellore
B.Tech · Computer Science
August 1, 2022 – June 30, 2026
Army Public School, Jalandhar Cantt
Class XII · Science
June 1, 2021 – May 31, 2021
Army Public School, Jalandhar Cantt
Class X · General
June 1, 2018 – May 31, 2019
RedoQ Software Services Pvt. Ltd.
Web Developer - Intern
May 1, 2025 – July 31, 2025
India
SentinelOps: Agentic LLM Copilot for SRE Incident Response
June 19, 2026 – Present
Constructed an end-to-end MLOps platform fine-tuning Mistral-7B-Instruct with QLORA (4-bit NF4, rank-16 LORA) on 2,500+ self-compiled postmortems over 21 GPU-hours (PyTorch, PEFT, TRL, W&B, MLflow); 0.63 faithfulness on a held-out set graded by a Llama-3.3-70B LLM-as-judge, with failure modes in a published HF model card. Built a two-stage RAG agent in LangGraph orchestrating tools (Prometheus, runbook search, postmortem drafting) over a 2,579-vector Qdrant index with BGE embeddings + a cross-encoder reranker; served via FastAPI + WebSockets with VLLM AWQ inference on Modal. Shipped a TensorFlow/Keras DistilBERT baseline and a Ragas + LLM-as-judge eval suite in a GitHub Actions CI eval-gate; deployed to Kubernetes via Helm + ArgoCD GitOps with Prometheus/Grafana observability and Kafka (Redpanda) ingestion.
View ProjectSafety Vision: Open-Source AI Workplace-Safety Monitor
June 19, 2026 – Present
Fine-tuned a YOLOv8s PPE detector (ONNX, OpenCV, CPU-only 500-800 ms) on 80k+ images with Albumentations augmentation; 0.763 mAP@50 on a held-out test set, exported for serverless inference. Built dual explainability (GradCAM + SHAP) and OSHA-grounded incident reports via a multimodal Gemini Flash LLM with RAG (Qdrant + BGE over 29 CFR standards) in LangGraph. Deployed 3 surfaces: Next.js 14/Vercel, Gradio/HF Spaces, and a serverless AWS API (Lambda Function URLs, S3, DynamoDB, ECR; Terraform; FastAPI) with a PyPI SDK + CLI; added Prophet vs SARIMA forecasting, 3 A/B tests (Cohen's d = 0.65, McNemar p < 0.001), Supabase, and a GitHub Actions CI gate (142 tests, MLflow + W&B) at $0 runtime.
View ProjectIoT Crop Yield Prediction Architecture
June 19, 2026 – Present
Deployed a custom-tuned CatBoost model on a self-compiled 250,000-row dataset (about 98% test-set accuracy) for real-time crop-yield forecasting, with a Flask service preprocessing IoT sensor data (temperature, humidity, soil nutrients) via Pandas/NumPy. Optimized the MongoDB/NoSQL schema for high-volume ingestion, cutting inference latency from a 15s baseline to about 150 ms via efficient indexing.
View ProjectAWS Certified Cloud Practitioner
Amazon
June 1, 2026 – Present
GenAI Foundation
Microsoft
June 1, 2026 – Present
DevOps Professional
Oracle
June 1, 2026 – Present
MySQL Implementation
Oracle
June 1, 2026 – Present
Data Science Professional
Oracle
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's portfolio showcases a strong initiative through multiple personal projects that are highly relevant to the ML Engineer role. The diversity of projects (LLM copilot, IoT prediction, safety vision) demonstrates a broad interest and ability to apply ML solutions to different domains. The use of open-source tools and contributions (published HF model card, PyPI SDK) suggests a collaborative and community-oriented mindset. The candidate's academic background and certifications further reinforce a commitment to continuous learning and professional development, aligning well with a growth-oriented culture.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong ability to work on complex, end-to-end systems, suggesting good problem-solving and project management skills. The detailed technical descriptions imply clear communication of technical concepts. The breadth of technologies used across projects suggests adaptability and a willingness to learn new tools. However, without direct interview data, specific soft skills like teamwork, leadership, or stress handling cannot be fully assessed.