I am a highly skilled Full Stack Developer with over 12 years of experience specializing in web development. I have extensive expertise in PHP, Node.js, React, and Angular, working with frameworks and platforms such as Express.js, Next.js, and NestJS , Laravel, CodeIgniter, Yii2, CakePHP. I have built dynamic and scalable applications using React and Angular, leveraging modern front-end technologies to create seamless user experiences. Beyond PHP and JavaScript technologies, I have strong experience with Java (Spring Boot) and Python (FastAPI, Flask) and have worked extensively with AWS cloud services, allowing me to build scalable, secure, and efficient applications across diverse technologies. I have successfully completed over 80 projects across various sizes and technologies, delivering custom solutions including CRMs, eCommerce platforms, real estate portals, tour and travel systems, HRMS, hospital management systems, college CRMs, ERP solutions, school CRMs, inventory management, and payroll systems. My expertise spans web, desktop, and mobile applications for both iOS and Android platforms. I am also proficient in web design, using Bootstrap to create responsive, customized designs, and can efficiently convert PSD to HTML. Additionally, I have extensive experience in working with databases such as MySQL, PostgreSQL, MongoDB, Kafka, Pusher, Cassandra, and ClickHouse. Technical Skills: • Languages: PHP, JavaScript (Node.js, TypeScript), Java Spring boot, Python, C++ • Frameworks: Laravel, CodeIgniter, Yii2, NestJS, Express.js, CakePHP, Zend, Django, Java Spring Boot • Front-end Technologies: React.js, Next.js, Angular, Redux, TypeScript, Firebase, Firestore • CMS Platforms: WordPress, PrestaShop, OpenCart, Magento, Joomla, Shopify, Squarespace • Mobile Technologies: React Native, Ionic Framework, Cordova, PhoneGap • Databases: MySQL, PostgreSQL, SQLite, MongoDB, Kafka, Cassandra, Pusher • Development Tools: Jira, GitHub, GitLab, BitBucket, Atlassian, Trello, Slack • Client-Side Scripting: JavaScript, AngularJS, AJAX, jQuery, JSON, Bootstrap 3, OnsenUI • APIs: Microservices, RESTful Services, GraphQL • DevOps: EC2, RDS, Lambda functions, Load Balancer, Kubernetes, Apache, Nginx, CloudFront, S3, Cloudflare, CI/CD pipelines WordPress: Elementor Pro, Divi Theme, Visual Composer, WooCommerce, ListingPro, and site speed optimization Google PageSpeed Insights optimization GTmetrix performance optimization Page load time optimization With this diverse skill set, I excel at optimizing performance and delivering high-quality, scalable solutions tailored to meet the unique needs of clients.

Fullstack Software Engineer with 9+ years of professional experience, focused on software architecture, data analysis, and end-to-end product delivery. Experienced building scalable web applications and data pipelines using Typescript, Python, Next.js, Prisma, Supabase, Dagster and Clickhouse, with strong expertise in agentic architecture and generative AI (Claude Anthropic API, OpenAI, GeminiAI, and LLM-based workflows). Skilled in integrating third party services (Klaviyo, Hubspot, Granola, Stripe, Shopify, Plaid, N8N, and others), optimizing data processes, and delivering internal tools and MVPs. Solid background in Golang, NestJS, React, Flask, FastAPI, AWS, and agile practices (SCRUM, sprint planning, and QA). Recognized for leadership abilities, excellent communication skills, teamwork, the ability to work under pressure, quick and continuous learning, self-learning, efficiency, effectiveness, quality, and strong interpersonal relationships.

I build production-ready AI + RevOps systems around Salesforce/HubSpot— orchestration, fine-tuning, MCP/semantic layers, RAG, and custom apps—so your sales & marketing teams ship faster and close more. 15 years in GTM systems; expert-vetted; 100% JSS; enterprise-grade delivery. What I Do AI Orchestration & Agents: Workflow design, tool use, evaluation, guardrails. Model Work: Prompt libraries, fine-tuning, preference optimization, evals. MCP & Semantic Layer: Domain schemas, tool servers, retrieval interfaces. RAG over GTM Data: Vectorizing Salesforce/HubSpot, docs, tickets, logs. Custom Apps & Integrations: Node/TS, React, FastAPI; SFDC/HubSpot apps; data pipelines. Signature Offers (fixed-scope starters) RAG QuickStart (2–3 weeks) – Ingest SFDC/HubSpot + docs → eval’d RAG with dashboards and red-team tests. MCP + Semantic Layer Blueprint (10 days) – Define tools, schemas, safety, and a reference MCP server. RevOps AI Tune-Up (2 weeks) – Lead/opp hygiene, scoring, routing, SDR copilot prompts + analytics. Salesforce/HubSpot AI Add-Ons – Case summarization, auto-notes, email drafting, knowledge search. Fine-Tune Sprint – Data curation, SFT or adapter-based finetune, eval harness, rollback plan. Recent Wins (abridged) Sales SDR Copilot: triage + drafting on HubSpot → 35% faster replies, 12% lift in meeting creation. SFDC Knowledge RAG: MCP tools + policy guardrails → deflection up 18%, CSAT +0.4. Quote Assist: approvals + terms via LLM tools → cycle time −27%, fewer reworks. GenAI Data Layer: semantic layer on CRM + docs → GTM analytics in minutes, not days. Tech & Stack LLMs/AI: OpenAI, Anthropic, HF; RAG Semantic/MCP: Model Context Protocol, tool servers, function/tool calling Data: Postgres, ClickHouse, Redis, dbt, Snowflake, SQL, etc. Apps/Infra: Node/TS, React, Docker, GCP/AWS/Azure, any and all APIs CRM: Salesforce (Admin, App Builder, Sales/Service Cloud), HubSpot (RevOps/Automation) CRM App Dev: Custom and marketplace apps for Salesforce and HubSpot Who I’m Best For B2B SaaS, GTM-heavy orgs, public sector/regulated, field ops—teams with Salesforce/HubSpot that want measurable, production AI (not demos). How We Work Consultation (60–90 min): Objectives → success metrics → data/readiness check. Design Sprint: Architecture, safety/guardrails, eval plan. Pilot: Ship something that moves a KPI; then scale. Credentials / Certifications: MBA (Operations). Salesforce Data Architect, Admin, App Builder, Sales/Service Cloud. HubSpot Certified. (Full list on request.) Message me with: your CRM (SFDC/HubSpot), the GTM KPI to move, and target timeline. I’ll suggest a 2-week pilot that proves value fast.

I build production-grade full-stack systems and AI platforms — not prototypes, not demos. Currently working as a Software Engineer at Onloop (Singapore), where I architect Kai-Go: a multi-agent AI platform handling 400+ workflow invocations per second across 40+ microservices, routing traffic across 7 LLM providers (GPT-4o, Claude Sonnet, Gemini) with unified auth, cost governance, and real-time observability. 13 years of full-stack engineering across SaaS, enterprise, mobile, and AI — with real clients, real scale, and real uptime requirements. --- WHAT I BUILD WELL ✅ AI systems that actually work in production — multi-agent orchestration (LangGraph, Restate, Temporal), RAG pipelines with pgvector, LLM API gateways, context engineering, streaming responses, cost optimisation ✅ Backend APIs and platform architecture — NestJS, Node.js, FastAPI (Python), REST and GraphQL, multi-tenant SaaS, JWT/OAuth2 auth, RBAC, Redis caching, distributed locking, idempotent payments ✅ Frontend — React, Next.js (App Router), TypeScript, React Native. 19 published iOS and Android apps with a 4.9★ average rating and thousands of downloads ✅ Databases — PostgreSQL (primary), MySQL, Redis, pgvector, ClickHouse. Strong SQL, schema design, indexing, and query optimisation (reduced production queries from 4.2s to under 800ms) ✅ Cloud and DevOps — AWS (Lambda, EC2, API Gateway, S3, RDS, Bedrock), Docker, Kubernetes, GitHub Actions CI/CD, Terraform ✅ Integrations — payment gateways (Stripe, Razorpay), third-party APIs, webhook processing, event-driven architecture, real-time WebSockets --- WHAT WORKING WITH ME LOOKS LIKE I've delivered for 800+ enterprise clients across ERP, POS, hospitality, and SaaS platforms. I write architecture documents before I write code. I ask the questions that prevent rewrites. I stay for the long term — most of my client relationships run years, not weeks. I'm most useful when you're building something that needs to scale, integrate with external systems, or use AI in a way that actually works outside of a Jupyter notebook. Open to long-term contracts. Prefer projects with real technical depth.

AI Engineer & Data Automation Specialist I build production-ready AI agents and data platforms that solve real business problems. With 6 years of data engineering experience and 1 year focused on Agentic AI, I ship systems that run in production—not just demos. What I Deliver: AI Automation & LLM Integration: Custom agents for customer support, internal data analysis, and document retrieval (RAG). Data Engineering & BI: End-to-end pipelines (ClickHouse, Snowflake, BigQuery) and dashboards (Power BI, Tableau, Metabase). Scalable Systems: Solutions serving millions of users with 95%+ CSAT and rigorous evals/tracing. Recent Production Impact: Customer Support Agent: Handles 10,000+ daily conversations for a 4M+ user platform. Fintech Data Bot: Acts as the main analyst, writing and running SQL for regulated data. HR Platform: Built solo in 5 weeks using parallel AI coding sessions. Stack: Python, TypeScript, LLM APIs (Claude, OpenAI), PydanticAI, Vercel AI SDK, SQL, Airflow, dbt. Freelancing on Upwork since 2021 with 100% Job Success and 35+ clients. Tell me what you want automated, and I will tell you honestly if an agent is the right tool.

I build data pipelines and the LLM features that run in a production environment. ETL and warehousing, Machine Learning, RAG, document/data extraction. Backend + ML + data engineer, fluent English, US-timezone-friendly. - Data pipelines & warehousing - RAG over your docs - Document & data extraction - LLM Integrations Stack: Python, FastAPI, PostgreSQL/pgvector, ClickHouse, Snowflake, Dagster, Qdrant, Redis, Docker, Claude & OpenAI APIs. I'm an engineer by training, so I build for what happens when things go wrong: validation, fallbacks, and a human in the loop wherever a mistake would be expensive. I work async, communicate clearly, and ship in days, not months. Send me what you're trying to automate and I'll tell you honestly whether AI is the right tool for it.

I build production-grade distributed systems, event-driven platforms, AI-enabled applications, and backend infrastructure that can scale beyond the prototype stage. With 13+ years of software engineering experience, my background spans high-volume messaging platforms, asynchronous architectures, SaaS communication systems, backend APIs, observability, reliability engineering, and modern AI application development. Most AI projects fail not because of the model, but because of poor system architecture, weak operational design, and fragile integrations. My focus is building AI-enabled systems that remain maintainable, observable, and reliable in production. 🚀 WHAT I BUILD ### Distributed Systems & Backend Platforms I design and build backend services that handle asynchronous workloads, messaging pipelines, API integrations, retries, idempotency, observability, and operational scalability. Examples include: • Event-driven architectures using Kafka and queue-based processing • High-throughput messaging and communication platforms • Microservices and API ecosystems • Reliability and observability improvements • Backend modernization and platform evolution initiatives --- ### RAG (Retrieval-Augmented Generation) Systems I build retrieval systems that combine vector search, document retrieval, orchestration logic, and LLM generation. Recent work includes OmniRAG, an open-source RAG platform built in Go featuring: • ChromaDB, Qdrant, Pinecone, and MongoDB backends • Claude and Ollama integration • Retrieval orchestration and routing • PDF ingestion pipelines • Streaming retrieval workflows • Vector database evaluation and benchmarking --- ### Agentic AI & MCP Systems I build agent-based systems that can discover tools, execute workflows, and interact with external systems. Recent work includes: • MCP-powered agents • ReAct-style agent architectures • Tool discovery and execution frameworks • Document search and retrieval agents • Database-integrated agent workflows • Claude and Ollama powered agent systems --- ### AI-Native Engineering Workflows I actively use AI-assisted engineering workflows for: • Architecture design • Development acceleration • Code reviews • Migration planning • Debugging and root-cause analysis • Operational investigations • Documentation generation My focus is practical engineering productivity rather than AI hype. --- 🛠 TECHNOLOGIES Languages: • Golang • NodeJS • PHP Backend & Infrastructure: • Kafka • Redis • MongoDB • ClickHouse • Kubernetes • Docker • REST APIs • Microservices AI & Retrieval: • Claude Anthropic | Codex | Antigravity • Ollama • MCP • RAG • ChromaDB • Qdrant • Pinecone • Chunking | Embeddings • Agent Architectures Observability & Operations: • Datadog • Grafana • Logging • Metrics • Distributed Tracing • Reliability Engineering --- 💡 WHY CLIENTS WORK WITH ME You're not hiring a prompt engineer. You're hiring an engineer who understands how production systems behave under load, how distributed systems fail, how to build reliable backend architectures, and how to integrate modern AI capabilities into real applications. Whether you're building a scalable backend platform, a RAG application, an agent workflow, or an AI-enabled product, I can help design, build, and operationalize it with a strong engineering foundation.

I'm a backend-focused full-stack engineer who builds scalable SaaS platforms that hold up under real load. Whether you need an event-driven backend, a multi-tenant architecture, or a reliable API, I can take it from schema design to deployment. - Specialized in Node.js / NestJS, Kafka, PostgreSQL, Redis, and React — 2.5+ years shipping production systems - Built an in-house ATS from scratch: 3× throughput gain, 70% smaller Docker images, zero extra infrastructure - End-to-end ownership — I ask questions before writing code and communicate proactively throughout

I'm a Full Stack Software Engineer with 4+ years of experience building scalable web applications, SaaS platforms, REST APIs, and high-performance backend systems using Laravel and modern JavaScript frameworks. I help startups and businesses build reliable, maintainable, and scalable products—from MVPs to enterprise applications. What I can help you with Laravel (9/10/11/12) application development PHP 8.x backend architecture REST API & third-party API integrations Multi-tenant SaaS applications React.js & Next.js frontends PostgreSQL, MySQL & Redis optimization Authentication, authorization & role management Payment gateway integrations (Stripe, Razorpay, PayPal) Queue systems, background jobs & scheduled tasks Docker, AWS deployment & CI/CD Performance optimization and database tuning Bug fixing, code refactoring and legacy Laravel upgrades Experience Over the past four years, I've worked on production applications used by thousands of users, including: Travel CRM and reservation platforms Enterprise business management systems E-commerce platforms Real-time tracking applications Content management systems AI-powered applications and MCP (Model Context Protocol) integrations I've built systems that process large datasets, optimize complex database queries, implement caching with Redis, design scalable APIs, and integrate monitoring tools like Datadog and Sentry to ensure production reliability. My Development Approach Clean, maintainable, and well-structured code Performance-first architecture Secure and scalable backend design Clear communication and regular project updates Long-term maintainability over quick fixes Thorough testing before delivery Whether you need a new SaaS product, backend API, Laravel application, or help scaling an existing system, I can deliver production-ready solutions that are built to last. Let's discuss your project and turn your idea into a reliable, scalable product.

🌟 Full Stack AI Engineer | End-to-End Intelligent Systems Architect | Production ML/LLM Specialist Transforming unstructured data into enterprise-grade AI solutions. I architect, build, and deploy production-ready AI systems across the complete ML pipeline: from data engineering and model optimization to MLOps infrastructure and full-stack deployment. Specializing in document intelligence automation, retrieval-augmented generation (RAG), and multimodal LLM applications. CORE EXPERTISE: 🔹 End-to-End Document Intelligence & Automation → Advanced OCR with transformer-based models (TrOCR, PaddleOCR, Azure Vision API) → Intelligent PDF extraction using vision transformers and semantic segmentation → Layout-aware document parsing with graph neural networks (GNNs) and attention mechanisms → Batch processing pipelines with asynchronous job orchestration and fault tolerance → Handwriting recognition and synthetic data augmentation for improved generalization 🔹 Large Language Models & Retrieval-Augmented Generation (RAG) → Production RAG systems with dense vector retrieval and semantic reranking → Fine-tuning LLMs (LoRA, QLoRA, full fine-tuning) for domain-specific tasks → Multi-modal LLM applications integrating vision-language models (CLIP, LLaVA, GPT-4V) → Prompt engineering and chain-of-thought reasoning for complex reasoning tasks → Vector database optimisation (FAISS, Qdrant, Pinecone, Milvus) with approximate nearest neighbor search → Context window optimisation and token-efficient inference techniques 🔹 Full-Stack Backend Architecture & API Development → RESTful API design with FastAPI, async patterns, and performance optimization → Microservices architecture with containerization (Docker, Kubernetes) → Authentication, authorization, and security hardening (OAuth2, JWT, encryption) → Database design: relational (PostgreSQL), NoSQL (MongoDB), vector DBs, graph DBs → Real-time data streaming and event-driven architectures (Apache Kafka, Redis) → API gateway patterns, rate limiting, and traffic management 🔹 Machine Learning Operations & Production Systems → ML pipeline orchestration (Airflow, Prefect) with data lineage tracking → Model registry and versioning (MLflow, Weights & Biases, DVC) → A/B testing frameworks and experiment management → Continuous integration/continuous deployment (CI/CD) for ML models → Model monitoring, drift detection, and automated retraining pipelines → Feature engineering, feature stores, and data quality management 🔹 Advanced Deep Learning & Computer Vision → Object detection (YOLO, Faster R-CNN, EfficientDet) with real-time inference optimization → Semantic segmentation and instance segmentation (Mask R-CNN, DeepLabV3) → Image classification with transfer learning and domain adaptation techniques → Real-time video analytics pipelines (NVIDIA DeepStream, OpenCV) → Model compression: quantization, pruning, knowledge distillation for edge deployment → TensorRT optimization for GPU inference acceleration (40%+ speed improvements) 🔹 Generative AI & Creative Workflows → Diffusion models and latent diffusion implementation (Stable Diffusion, SDXL) → Workflow automation (ComfyUI) for image generation and video synthesis → Text-to-image and image-to-image generation with fine-tuned models → Multimodal generation pipelines and conditional image synthesis 🔹 Cloud Infrastructure & Deployment → AWS (EC2, S3, Lambda, SageMaker, RDS), Azure (Blob Storage, Cognitive Services), GCP → Serverless deployments (AWS Lambda, Azure Functions, RunPod Serverless) → GPU cluster management and distributed training (PyTorch DDP, NVIDIA NCCL) → Infrastructure as Code (Terraform, CloudFormation) and GitOps workflows RESULTS & IMPACT: ✅ Production Deployments: 10+ enterprise AI systems deployed to production ✅ Performance Optimization: 40%+ cost reduction through TensorRT quantization and inference optimization ✅ Accuracy Metrics: 99%+ precision on OCR tasks through transformer-based architectures ✅ Scalability: Architected systems processing 100k+ documents monthly ✅ Time Savings: Clients report 80%+ reduction in manual data entry workflows ✅ MLOps Excellence: Zero-downtime deployments with automated monitoring and alerting TECH STACK (Production-Grade): AI/ML Frameworks: PyTorch, TensorFlow, JAX, HuggingFace Transformers, LangChain, LlamaIndex NLP: Sentence-Transformers, spaCy, NLTK, Prompt Engineering, LLM fine-tuning (LoRA/QLoRA) Vision: OpenCV, Pillow, torchvision, YOLO, Faster R-CNN, SAM (Segment Anything Model) OCR: Tesseract, EasyOCR, PaddleOCR, TrOCR, Azure Vision, Google Vision API Vector DBs: FAISS, Qdrant, Pinecone, Milvus, Weaviate, ChromaDB Backend: FastAPI, Flask, Django, async/await patterns, WebSockets Databases: PostgreSQL, MongoDB, Redis, ClickHouse, graph DBs (Neo4j) MLOps: MLflow, Airflow, Prefect, DVC, Weights & Biases, GitHub Actions Deployment: Docker, Kubernetes, AWS, Azure, GCP, RunPod, Railway, Render Generative AI: ComfyUI, Stable Diffusion, ControlNet, CLIP, DALL-E