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I am the Founder and AI Architect of an applied AI consultancy (Vantablan) where we design and deploy production grade machine learning systems that actually run in real environments. I personally lead the technical architecture and delivery, and we have over 4 years of hands on experience building computer vision and generative AI systems across sports analytics, healthcare, e commerce, and SaaS products. Our work is end to end. We take ownership from problem definition to deployment and long term stability. On the computer vision side, we build full pipelines around object detection, instance segmentation, pose estimation, and identity recognition. These systems run on live video streams and real world inputs, not research demos. I design the core model architecture and inference logic, while we optimize pipelines using PyTorch based models, YOLO style detectors, OpenCV, and modular deployment stacks focused on speed and reliability. In generative AI, we work on LLM fine tuning, controlled generation systems, and diffusion based pipelines used for automation, internal tooling, and content workflows. I stay closely involved in prompt design, evaluation logic, and model selection to ensure outputs are predictable and usable in production. We do not build demo chatbots. We build systems that plug directly into products. Beyond modeling, we own the AI infrastructure. I handle system design while we implement training pipelines, CI CD workflows, containerized deployments, and monitoring across AWS, GCP, and Azure so models ship cleanly and remain stable after launch. I also bring a strong research background with published work in deep learning and reinforcement learning, which informs our design decisions when rigor is required. Still, our priority is simple. Build systems that work, scale, and deliver measurable outcomes. Clients work with us when they need a team that can own the AI side of their product, make architectural calls, and deliver without hand holding.

Proud partner with NVIDIA Inception + MIcrosoft Startups ⭐⭐⭐⭐⭐ "Amazing work with Nitish, very professional and glad..." ⭐⭐⭐⭐⭐ "Professional and with great expertise. A pleasure to work with!" ⭐⭐⭐⭐⭐ "Great attention to detail and v responsive..." ⭐⭐⭐⭐⭐ "Very good developer, Really he saved my time and solved my problem in very short time. Best developer ... on Upwork" ════════════════════════ 🏆 UPWORK ACCREDITATIONS & ACHIEVEMENTS ✅ Top Reinforcement Learning Freelancer in India ✅ Top 5 Computer Vision Engineer in India ✅ Top 10 Machine Learning Engineer in India ✅ Top 10 Deep Learning Experts in India ✅ Best Robot Operating System (ROS) Developers in India ✅ Top PyTorch Freelancer in India ✅ Top 20 Robotics Agency (Robolabs AI) ✅ 100% Job Success Rate with perfect client satisfaction ✅ Top Rated Status with 5-star client reviews ════════════════════════ ⚡ EXPERTISE & SERVICES Specializing in high-performance AI/ML solutions using Python, C++, and MATLAB 🔹 Machine Learning & Reinforcement Learning Chatbots • Recommendation Engines • Autonomous Decision-Making Systems 🔹 Robotics & Autonomous Systems Real-time Localization • Robotic Control • Motion Planning 🔹 Computer Vision Applications Autonomous Vehicles • Surveillance • Quality Control Systems 🔹 Cloud AI Deployment Scalable Solutions on AWS, GCP, Azure • Docker • Kubernetes 🔹 Quality Assurance Rigorous Testing • Top-Performing Models • Enterprise Reliability ════════════════════════ ⚡ TECHNICAL SKILLS 🤖 Robotics: ROS, ROS2, Isaac Sim, SLAM, RL, Gazebo, MoveIT, RoboDK, RPA, End-to-end Learning, Sim2Real, Digital Twin, Robotics Sensors (LiDAR, Stereo Camera, IMU, Odom) 🧠 LLM Development: Supervised Fine-tuning, DRPO with state-of-art models (DeepSeek, o1, o3-mini, Claude Sonnet, Grok, Google Gemini, Mistral) 📊 Machine Learning & Deep Learning: TensorFlow, PyTorch, Keras, Scikit-learn, StableBaselines, XGBoost, LightGBM 💬 NLP: spaCy, NLTK, Hugging Face Transformers, OpenAI API 👁️ Computer Vision: VLMs, OpenCV, YOLO, SAM, SAM2, Point Cloud Library (PCL), Dlib 🎯 Reinforcement Learning: OpenAI Gym, RLlib, Unity ML-Agents 📈 Data Science & Big Data: Pandas, NumPy, Dask, Apache Spark, Hadoop, Presto 🗄️ Data Storage: SQL, MongoDB, Cassandra, Neo4j, Dgraph ☁️ Cloud & DevOps: AWS, GCP, Azure, Docker, Kubernetes, Terraform, CI/CD (Jenkins, GitLab CI) 🚀 AI Deployment: ONNX, TensorFlow Serving, NVIDIA Triton, MLflow, FastAPI, Flask 💻 Programming Languages: Python, C++, C, MATLAB 🌐 Web Frameworks: React, Node.js, Flask, Django 🔧 Automation & CRM: Zapier, Make, Salesforce, HubSpot, Airtable, Notion ⚙️ Other Tools: Apache Kafka, Redis, RabbitMQ, Elasticsearch, Prometheus, Grafana ════════════════════════ ⭐ WHY CHOOSE ROBOLABS AI? 🏆 Award-Winning Excellence Backed by Microsoft • Trusted Global Leader • Recognized for Consistent Quality 🌍 Global Presence, Local Commitment Team Members Worldwide • Seamless Support • Round-the-Clock Availability 📊 Proven Track Record 100+ Successfully Delivered Projects • AI-Driven Automation • Intelligent Systems ════════════════════════ ✨ WHAT SETS US APART 🏢 Enterprise-Grade Solutions: From MVP to production systems handling millions of users 🔄 Complete AI Implementation: End-to-end development, deployment, and maintenance 🎯 Industry Expertise: 100+ successful projects across Fortune 500 companies and fast-scaling startups 🚀 Cutting-Edge Technology: Using 2025’s breakthrough AI models and frameworks ════════════════════════ At Robolabs AI, we blend technical expertise with industry insights to create AI solutions that transform your business.

AI Engineer | Computer Vision, Deep Learning & AI Chatbot/Voice Agent Development I am a software and AI engineer with 7+ years of experience, starting in computer vision and deep learning and now working with my team on AI chatbots and voice agent systems. Early in my career, I built computer vision and deep learning solutions including an age progression image generation engine and model conversions from API-based systems to TensorFlow. This gave me a strong foundation in Python, TensorFlow, PyTorch, and production machine learning, which I still rely on today. Over the last couple of years, working with my team at Webtunix AI LLP, I have contributed to building 20+ RAG-based chatbot systems for clients across different industries. Most of this work is covered under client NDAs, so I am not able to share specific project names or details publicly, but I am glad to walk through my approach, architecture decisions, and technical depth directly in an interview or call. What I work on: Computer vision & deep learning Image generation models, computer vision pipelines, API-to-TensorFlow model conversion, neural network development, and classification/forecasting systems using TensorFlow and PyTorch. RAG-based AI chatbots & voice agents Building retrieval-augmented chatbot systems using vector databases (Pinecone, ChromaDB, FAISS), OpenAI/Claude API integration, LangChain-based pipelines, document Q&A systems, and voice agents with speech-to-text/text-to-speech (Twilio). Backend & deployment Python, FastAPI, Flask, Django, PostgreSQL, Docker, Kubernetes, AWS/GCP/Azure, and cloud migrations (including Heroku to GCP). Tools & frameworks: Python, TensorFlow, PyTorch, Scikit-learn, OpenAI API, Claude, LangChain, Pinecone, FastAPI, Django, Twilio, Docker, Kubernetes, Git Why clients work with me: I bring a solid engineering foundation from years of computer vision and machine learning work, combined with hands-on experience building 20+ RAG-based chatbot and voice agent systems as part of an active team. I communicate clearly, give regular progress updates, and care about delivering something that actually works after the project ends, not just in a demo. If you need a computer vision/ML solution, or want to explore an AI chatbot or voice agent for your business, I would like to hear about your project.

Most people building LLM products have never trained one. That's why the fixes stop at the prompt, when retrieval quietly degrades, cost per conversation triples, or quality regresses, and nobody notices. The problem is underneath the API, and that's where I work. Five years training language models, and five years shipping systems built on them. That combination is why teams call me when what they already built stops holding up. WHAT I DO 1) Production LLM systems - RAG, agents, serving Retrieval that actually retrieves: 92% retrieval accuracy on a LlamaIndex + Weaviate pipeline with a 20% cut in query time. Chunking, embedding choice, hybrid and reranked retrieval - and an eval set that proves the change helped instead of a vibe check. Agent systems with a cost and latency budget: multi-agent pipelines in LangChain / LlamaIndex / CrewAI, tool calling, long-running state. One automated product-information system cut manual review effort ~90%. Self-hosted and open-weight serving: GPU sizing, quantisation strategy, throughput and concurrency planning, and quality validation against a frontier baseline before you cut over. At Dell I shipped 4-bit GPTQ quantisation and SparseGPT pruning (~40% sparsity) for hardware-constrained inference. Latency and cost: replacing an LLM call with a fine-tuned 300M classifier took one production path from ~1s to ~100ms. Model routing, caching, honest per-request cost accounting. Evaluation and regression gates: offline eval sets, LLM-judge calibration, CI gates so a prompt or model change can't silently regress. Most teams I meet have no way to answer "is it better than last week." 2) Fine-tuning, post-training and alignment Pre-trained a 355M-parameter GPT-2-medium architecture from scratch on 28B tokens (Cosmopedia-v2), distributed across 4x NVIDIA H100's with DeepSpeed - mixed precision, gradient accumulation, LR scheduling. Beat the original GPT-2-medium checkpoint on perplexity. Improved Phi-4-14B-Instruct by 2% across every Hugging Face leaderboard benchmark via Model Stock merging, validated cheaply first on a LoRA-tuned Qwen2.5-1.5B proxy over 1.2M curated STEM samples. LoRA-tuned Qwen2.5-14B-Instruct on 12K reasoning samples, using synthetic data from a multi-agent generation pipeline - measured gains on GSM8K, GPQA, and MMLU. LoRA + DPO on Llama-2 for customer-care summarisation (23K SFT samples, 5K preference pairs): 17% better across evaluation metrics. Designed and ablated a novel Drift-Diffusion attention mechanism on BERT-base, with full Weights & Biases tracking across baseline, unscaled and gated variants. The honest version: most projects that arrive asking for a fine-tune don't need one. The base model was already good enough, the eval set couldn't detect improvement, or the problem was retrieval. I'll tell you which before you spend GPU budget - that answer is worth more than the training run. STACK Python, PyTorch, Hugging Face, DeepSpeed, Weights & Biases, FastAPI, LangChain, LlamaIndex, CrewAI, Weaviate, Elasticsearch, MongoDB, Docker, Kubernetes, AWS (EC2, Inferentia-2), GCP. OpenAI, Anthropic, Gemma / Llama / Mistral / Qwen / Phi. ElevenLabs and LiveKit for voice. BACKGROUND ML Engineer at Dell Technologies and BYJU'S AI Labs, where a multi-objective Transformer recommender I built served 1M+ students across 1B+ data points at 85% F1. Contributor to Hugging Face Transformers documentation and to DocsGPT. Top-Rated on Upwork with a 100% Job Success Score. WHO I'M NOT FOR If the job is wiring Zapier or n8n between two SaaS tools, hire a generalist - genuinely, you'll get a better deal and a faster one. I'm worth the rate when the system has to be correct, cheap and measurable under real traffic. HOW TO START Start with the fixed-fee diagnostic rather than an open-ended hourly build. I read your pipeline, your traces and your evaluation setup, then send a written diagnosis: where quality is leaking, what each request actually costs, what to fix first, and what fixing it takes. It stands on its own as a deliverable, and it becomes the scope if you want me to do the build. Send me what's breaking and one example of the wrong output. That's enough to start.

Experienced data scientist with over 4 years of experience in machine learning projects. Active participant on Kaggle, a google backed platform for data science competitions, rated within Top 1% data scientists globally. Have experienced in deep neural networks, convolution neural networks, recurrent neural networks, genetic algorithms, natural language processing, support vector machines, and generative adversarial networks. 𝐒 𝐊 𝐈 𝐋 𝐋 𝐒 ------------------------- 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 : - CNN, - DNN, - RNN/LSTM, - GAN, - Transformers, - Auto-Encoders, - HMM-GMM - SVM, - Boosting, - Random forest, 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 : - C, - C++, - Python, - R, - Matlab, - CUDA(Beginner). 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸/𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 : - Tensorflow, - Tensorflow lite, - Tensorflow js, - Keras, - Pytorch, - RASA, - Pandas, - Scikit-learn, - Open CV, - Tesseract OCR, - Kaldi, - OpenFace, - SpaCy, - NLTK, - Open Pose, - Others as well based on experience. 𝗧𝗼𝗼𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 : - Jupyter-notebook - Google Colab - Pycharm - R-Studio 𝗣𝗮𝘀𝘁 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘄𝗼𝗿𝗸 : - Generating karyotype from Chromosome micro slide image. - Identify boundaries of wound and measurement of wound - Extensive object detection, Detection of Sky, Window, Glass, Tree, building. - automated tool for detecting norms and stereotypes in popular culture. - Deep Learning with Image processing for the transformation of style from one image to another. - Mobile embedded object detection model with TF-lite. - Developed an algorithm to generate a 3D model of faces from a single 2D mobile selfie using Python, Convolutional Neural Networks, PyTorch, and CUDA. - Created an algorithm for image and video compression using similarity between images with the help of OpenCV. - Developed an algorithm for the classification of different sounds of drones using MFCC and LPCC features and then SVM and HMM-GMM classifiers. - Created a default loan predictor algorithm with 99.4% accuracy using Python and deep neural networks. - Created an optical character recognition for English language using Python and OpenCV,. - Extracted the Legos from the videos and the classify it into 52 different classes using the convolutional neural network using Python. - Worked on the CNN based binary text classification for the movie reviews to identify the positive and negative reviews with neural networks and Python. - Developed an algorithm for moving object detection, which can find a moving object in a vibrant environment using deep neural networks, OpenCV, and Python. - Many other projects I have worked on. Few of them are signed with NDA. I shall be glad to provide any information/clarification that you might need to make a better decision. If you like my work. Please Direct Message me OR invite me to your job. I will happy to connect and take your project to success. Thanks for taking the time to read through my profile. Looking forward to talking to you. Thanks

Hello, my name is Gunjan. I have a handful of professional experiences and research fellowships and have been practicing programming and Data Science for over 4 years. My main interests lie in Machine Learning, Deep Learning, Computer Vision, and NLP/NLU, Data Analytics; I consider these to be my forte and have also published papers related to AI and Data Science in top journals on the same. My expertise is in the following domains: 1) Data Science 2) Artificial Intelligence 3) Machine Learning 4) Deep Learning 5) Natural Language Processing 6) Computer Vision 7) Data Analytics 8) Statistics 9) Data Structures & Algorithms