Top RAG Engineers

Currently contributing as a Technical Intern at Ellucian, leveraging skills in knowledge graph databases, graph-RAG, vector databases, and embeddings. With a strong academic foundation in computer science with data analytics from KPR College of Arts Science and Research, the work integrates theoretical knowledge with practical applications. Actively enhancing expertise through certifications, including deep learning for NLP and advanced AI engineering. Passionate about applying emerging technologies to solve complex challenges, with a focus on data-driven innovation and continuous learning.

I’m a Software Engineer with 3 years of professional experience in machine learning, computer vision, end-to-end pipeline development. Over the past few years, I’ve worked on projects that span the entire ML lifecycle — from training and fine-tuning deep learning models, to deploying them in production with frameworks like NVIDIA DeepStream, Openvino, and Triton Inference Server, to building supporting data pipelines with python, fastapi, SQL and NOSQL databases.What excites me most is taking a research idea or prototype model and turning it into a reliable, scalable system that delivers real-world impact. I’ve built real-time object detection solutions for applications like license plate recognition and safety monitoring, optimized inference pipelines to cut latency and streamline deployment.Recently, my focus has expanded into Generative AI and Large Language Models (LLMs). I’m exploring techniques like Retrieval-Augmented Generation (RAG), prompt engineering, LLM fine-tuning, Agent building to apply foundation models to enterprise and custom use cases. My background in traditional ML and computer vision gives me a strong technical base, while my hands-on software engineering experience ensures I think beyond models — into usability, scalability, and maintainability.Looking ahead, I aim to specialize in AI/ML engineering roles that combine deep technical work with practical deployment at scale. Whether it’s optimizing inference pipelines, designing end-to-end GenAI systems, or building AI-powered applications, I’m driven by the challenge of making cutting-edge technology accessible, reliable, and impactful.

I'm a software engineer with 3 years of experience building full-stack applications on the MERN stack. Over the last year I shifted into AI Engineering building GenAI systems that are reliable, controllable, and actually hold up in production My current work centers on Retrieval-Augmented Generation and the unglamorous parts that make it work: * RAG pipelines — chunking, embedding, and retrieval that stay grounded instead of hallucinating * Vector search — FAISS / embedding stores, tuning recall vs. latency, keeping context relevant * LLM orchestration — prompt structure, routing between models, tool-calling, controllable outputs * AI system design under real constraints — latency budgets, accuracy floors, cost, and offline/edge limits * Evaluation — measuring whether the system behaves, not just whether it responds I care less about what a model can do in a one-shot demo and more about how it behaves under real usage messy inputs, scale, and edge cases. Currently building GenAI systems and sharing the engineering decisions in public the trade-offs, the dead ends, and what actually shipped.

Software Developer specialising in AI and backend systems, experienced in building intelligent applications using Python, FastAPI, ML and Gen AI.

Machine Learning Engineer, Python Developer, and Data Analyst passionate about transforming data into intelligent solutions With deep expertise in Generative AI, Large Language Models, and enterprise-scale ML systems, I specialize in building production-ready AI applications that drive real business impact. CORE EXPERTISE: - Machine Learning & AI: TensorFlow, PyTorch, Scikit-learn, Hugging Face - Python Development: FastAPI, Flask, RESTful APIs, Microservices - Data Science: Statistical Analysis, Data Visualization, ETL Pipelines - Data Analytics: SQL, Statistical Modeling, ETL Pipelines, Power BI, Data Visualization - Generative AI: RAG Systems, LLM Fine-tuning, OpenAI Integration - Computer Vision: OpenCV, CNN, Image Processing, Real-time Detection - Cloud & MLOPs: AWS (EC2, S3, Lambda), Docker, CI/CD Pipelines RECENT ACHIEVEMENTS: - Engineered LLM-powered AI agents for financial workflows at Uptiq AI - Built medical diagnosis RAG system with 93% accuracy, reducing diagnosis time by 40% - Developed scalable subcontractor research platform with 40% faster candidate ranking - Created multimodal CNN for gesture/emotion detection with 89% accuracy READY TO CONTRIBUTE FROM DAY 1: - Strong coding skills in Python, C++, SQL with clean, maintainable code practices - Experience with Agile development, version control, and collaborative team environments - Proven ability to translate business requirements into technical solutions - Track record of delivering projects on time with measurable business impact MCA Graduate (CGPA: 9.16) | Currently seeking opportunities where I can drive innovation and deliver results. OPEN TO IMMEDIATE OPPORTUNITIES - Let's discuss how I can accelerate your team's success! #SoftwareEngineer #MachineLearning #DataAnalyst #Python #AI #HiringNow #TechTalent #Python #ComputerVision

I am a GenAI Developer specializing in building production-ready AI systems using Azure OpenAI, Azure AI Search, and cloud-native architectures. I design robust RAG pipelines, build agentic AI workflows, and turn unstructured data into intelligent business solutions. My experience combines backend engineering, cloud development, and applied AI. I have delivered enterprise-grade systems using Azure Functions, Azure Storage, Azure PostgreSQL, and vector-based search pipelines. I enjoy solving complex problems, optimizing LLM reasoning, and designing scalable architectures that deliver reliable and explainable AI outputs. I work across the full lifecycle of AI solutions — from ingestion pipelines, data preparation, retrieval optimization, to agent-based orchestration. I am passionate about the future of cognitive systems and building AI that feels more autonomous, context-aware, and useful. I’m currently seeking opportunities as a GenAI Developer / AI Engineer / LLM Engineer, where I can contribute to building advanced RAG systems, autonomous agents, and scalable AI applications.

I am an AI Engineer with 5+ years of experience specializing in designing and deploying enterprise-grade Generative AI solutions that drive significant automation and efficiency gains. My key strength is building and orchestrating advanced AI workflows, with deep expertise in Retrieval-Augmented Generation (RAG) and Agentic AI systems. I leverage frameworks like LangChain and LangGraph to connect Large Language Models (LLMs) with proprietary data, ensuring grounded, accurate, and context-aware responses. I am also skilled in LLM Fine-tuning techniques like LORA and PEFT.

Hello! As a Generative AI Engineer, I'm passionate about pushing AI boundaries to solve real-world problems. I leverage Large Language Models (LLMs), GANs, and Transformer Architectures to build intelligent, impactful AI systems that deliver tangible business value. Technical Expertise & Focus Areas: Generative AI & LLM Engineering: Expertise in architecting & optimizing end-to-end Retrieval-Augmented Generation (RAG) pipelines, designing efficient vector databases for semantic search, and applying advanced prompt engineering to maximize LLM performance. Model Optimization & Deployment: Excel at enhancing model efficiency via quantization (FP16, INT8), transfer learning, and robust deployment strategies, ensuring AI models are accurate, efficient, scalable, and production-ready. Computer Vision & Edge AI Systems: Proven track record developing & deploying real-time AI solutions on edge devices (NVIDIA Jetson), covering the full lifecycle from optimization to high performance and reliability. Deep Learning Architectures: Solid foundation & hands-on experience with advanced deep learning models (GANs, Vision Transformers), including training, evaluation, and performance acceleration. Key Contributions & Impact: Engineered & deployed scalable RAG pipelines, significantly enhancing LLM response accuracy & efficiency. Accelerated AI model inference by optimizing models for specific hardware (TensorRT on NVIDIA Jetson), achieving substantial performance gains. Led research & evaluation of open-source LLMs & embedding models, informing strategic tech adoption & driving data-driven decisions. Designed & implemented robust AI deployment strategies, ensuring seamless integration & continuous operation. Core Competencies: Generative AI: LLMs, RAG Architectures, Prompt Engineering, GANs, Transformers AI/ML Engineering: Deep Learning, NLP, Computer Vision, Model Optimization & Quantization, Real-time Inference Tools & Platforms: Python, PyTorch, LangChain, Hugging Face, Docker, Linux, Git, Vector Databases System Design: Scalable Solutions, Low-latency Deployment, Edge AI Integration Attributes: Strong problem-solving acumen, exceptional troubleshooting, and a proactive approach to continuous learning. I translate intricate technical concepts into clear, impactful solutions, a key asset in collaborative environments. Let's Connect: I welcome connections from fellow professionals and hiring managers. Reach out at [email protected] to discuss potential collaborations or impactful opportunities.

AI Engineer | Agentic AI & GenAI Specialist | Semantic RAG | MCP | LLMs & VLMs Software Engineer with overall 4 years of hands-on experience building and optimizing production-grade GenAI and deep learning systems, with strong expertise in Agentic AI architectures, Semantic RAG pipelines, and multi-agent communication using MCP and agentic protocols. Currently, I work with a leading fintech organization in India, where I design and deploy scalable Agentic AI systems for real-world financial use cases. My work focuses on: 1. Developing robust Semantic RAG frameworks 2. Building multi-agent orchestration systems 3. Implementing agent-to-agent communication via MCP 4. Optimizing LLM & VLM performance for enterprise deployment I bring a strong background in model fine-tuning, evaluation, and optimization, and have consistently delivered improvements in latency, efficiency, and system reliability for large-scale AI applications Core Expertise: Agentic AI & GenAI: Multi-Agent Systems, MCP, Agentic Protocols, Semantic RAG Models: LLMs, VLMs, Transformers Frameworks: PyTorch, Hugging Face, ONNX Runtime Optimization: Fine-Tuning, Quantization, Pruning Data & Tools: NumPy, Pandas, Matplotlib Programming: Python I thrive in fast-paced, high-impact environments where cutting-edge AI meets real-world production challenges. I am deeply motivated by building intelligent, autonomous AI systems that create measurable business value—especially in fintech and enterprise AI applications. Open to collaborating on: Agentic AI, GenAI systems, RAG architectures, and scalable LLM solutions. Let’s connect to build the next generation of intelligent AI systems.