10/12/2026 updated
100 % available
AI/ML Engineer | Computer Vision, LLM & NLP Automation, FastAPI Backends
Saharanpur, India
Worldwide
Integrated M.Tech in Artificial IntelligenceLangChainComputer VisionDjangoNatural Language ProcessingTensorFlowMatplotlibKubernetes
Computer Vision Model Development
Training and evaluating deep learning models for object recognition using TensorFlow, PyTorch, Keras, and OpenCV. Hands-on experience achieving 92% accuracy in object recognition tasks during an ML internship.
LLM & NLP Automation
Building agents that read unstructured text, extract key fields, and prioritize items by user preferences. Expertise in Groq AI, QLoRA fine-tuning, and synthetic dataset generation to reduce manual sorting and data entry.
FastAPI Backend & API Integration
Development of backend services connecting Google APIs, AI models, and frontends, packaged with Docker. Suited to automating data retrieval and processing workflows.
ML Pipeline Building & Optimization
Data generation, training workflows, and inference components, plus profiling and improving existing pipelines to reduce processing time by up to 25%.
Programming Languages
Proficiency in Python, C++, SQL, and MATLAB for a wide range of AI and software development tasks.
Data Analysis
Use of NumPy, Pandas, Matplotlib, and Seaborn for data manipulation, analysis, and visualization.
Cloud & DevOps Tools
Experience with Docker for containerization and deployment, Git/GitHub for version control, and Google Colab and Jupyter Notebook for experimentation.
Full-Stack ML Integration
Design of full-stack solutions integrating backend services, NLP models, IoT-device workflows, and frontend UIs, including 3D hardware simulators for device interaction demonstrations.
Training and evaluating deep learning models for object recognition using TensorFlow, PyTorch, Keras, and OpenCV. Hands-on experience achieving 92% accuracy in object recognition tasks during an ML internship.
LLM & NLP Automation
Building agents that read unstructured text, extract key fields, and prioritize items by user preferences. Expertise in Groq AI, QLoRA fine-tuning, and synthetic dataset generation to reduce manual sorting and data entry.
FastAPI Backend & API Integration
Development of backend services connecting Google APIs, AI models, and frontends, packaged with Docker. Suited to automating data retrieval and processing workflows.
ML Pipeline Building & Optimization
Data generation, training workflows, and inference components, plus profiling and improving existing pipelines to reduce processing time by up to 25%.
Programming Languages
Proficiency in Python, C++, SQL, and MATLAB for a wide range of AI and software development tasks.
Data Analysis
Use of NumPy, Pandas, Matplotlib, and Seaborn for data manipulation, analysis, and visualization.
Cloud & DevOps Tools
Experience with Docker for containerization and deployment, Git/GitHub for version control, and Google Colab and Jupyter Notebook for experimentation.
Full-Stack ML Integration
Design of full-stack solutions integrating backend services, NLP models, IoT-device workflows, and frontend UIs, including 3D hardware simulators for device interaction demonstrations.
Experience
Professional experience0 - 5 years
Freelancer since2025
Languages
EnglishNative speaker
Project history
Architected a multi-tenant SaaS backend using FastAPI and Google APIs to retrieve and parse placement emails in real time. Integrated Groq AI to build an agent that analyzes unstructured email text, extracts key job details, and ranks opportunities by user preferences. Building a web interface to visualize prioritized job alerts; preparing production-ready deployment with Docker. Technologies: FastAPI, Google APIs, Groq AI, Docker.
Developed a custom NLP engine and fine-tuned an LLM with QLoRA to interpret ledger commands accurately. Generated sector-specific synthetic datasets to train and test command understanding. Engineered end-to-end ML infrastructure including data generation pipelines, model training workflows, and inference components for real-time command processing. Designed a full-stack solution integrating backend services, NLP models, IoT-device workflows, and a frontend UI with a 3D hardware simulator. Technologies: Python, QLoRA, LLMs, NLP, full-stack integration.
Developed and trained a deep learning model for object recognition, reaching 92% accuracy. Collaborated with engineering leads to optimize existing ML pipelines, reducing model processing time by 25%.