Hire the Best Semantic UI Specialists

Deployed multiple AI products. Sub-200ms voice agent latency. 5x LangGraph optimization for Multi-Agent AI systems. See everything at - devbhangale.vercel.app You get one engineer who handles the AI, the backend, and the frontend. No handoffs. No coordination overhead. Just a complete system that ships. 🤖 AI Chatbots and Customer Support Agents- You get a LangGraph and LangChain-powered conversational agent with memory, tool calling, fallback handling, and a working eval harness baked in. Your bot handles real traffic, routes edge cases, and gives you measurable accuracy before it goes live. Integrates with OpenAI API, Gemini API, and Claude API depending on your latency and cost targets. 🧠 Multi-Agent AI Workflows- You get complex autonomous pipelines built with LangGraph, AgentSDK and CrewAI for research automation, financial analysis, document processing, and multi-step business decision workflows. Includes human-in-the-loop checkpoints, state management, and structured output generation. 🎙️ Voice AI Agents- You get a real-time inbound and outbound voice pipeline with sub-200ms response latency built on LiveKit and Google Cloud Vertex AI. It handles calls, qualifies leads, books appointments, and updates your CRM without a human on the other end. Powered by Deepgram STT, ElevenLabs or Sarvam AI TTS, and OpenAI Realtime API. 📚 RAG Systems and Knowledge Bases- You get a production Retrieval-Augmented Generation pipeline built on Qdrant with chunking strategy, semantic embeddings, hybrid search, and retrieval accuracy benchmarking. Your documents, product catalog, or internal knowledge base becomes queryable and accurate. Your team stops digging through PDFs. 🌐 Full-Stack Web Applications- You get a Next.js 15 (React 19) and FastAPI and PostgreSQL application with clean architecture and a UI that does not look assembled from a template. From MVP to production-grade SaaS with role-based access, multi-tenant support, and WebSocket-powered real-time features. 📈 Algorithmic Trading Systems- You get end-to-end trading pipelines integrated with live exchange APIs covering strategy backtesting, live signal execution, real-time data ingestion, and performance analytics dashboards. Notable builds: → Astrophage: LangGraph Vedic astrology AI platform. Reduced agent response time from 30 seconds to 6 seconds through a 5-node pipeline redesign. Qdrant RAG, Gemini Live API voice, 12 bound tools, multilingual support. Live at astrophageai.vercel.app → AI Voice Agent Platform: Sub-200ms real-time voice pipeline using LiveKit and GCP Vertex AI with SFU region pinning for Indian market latency. Deepgram STT, Sarvam AI TTS, WebSocket audio streaming. → FinAI: Multi-agent chartered accountant assistant using CrewAI. Automates capital gains (FIFO), HRA calculation, ITR schema mapping, and tax regime comparison with structured report generation. → InwiseIt: Production SaaS invoice platform with Next.js 15, FastAPI, PostgreSQL, role-based access, and shadcn/ui component architecture. → AI Ticketing System: LLM-powered triage system with automatic classification, priority scoring, and CRM handoff for business clients. → Algorithmic Trading Pipelines: Live exchange API integration with real-time signal execution, backtesting engine, and performance analytics. ⚙️ Tech Expertise: AI Agents and Orchestration: LangGraph, LangChain, CrewAI, Pydantic AI, OpenAI API, Gemini API, Claude API, AutoGen, LlamaIndex Voice AI: LiveKit, Google Cloud Vertex AI, OpenAI Realtime API, ElevenLabs, Deepgram, Retell AI, Vapi, Whisper, Sarvam AI RAG and Vector Search: Qdrant, Pinecone, ChromaDB, FAISS, semantic embeddings, hybrid search, document ingestion pipelines, retrieval benchmarking Chatbot Development: multi-turn conversation, tool calling, memory systems, structured outputs, function calling, LLM eval harnesses, prompt engineering Full-Stack Web: Next.js 15, React 19, FastAPI, PostgreSQL, Tailwind CSS, shadcn/ui, TypeScript, WebSockets, REST APIs Algorithmic Trading: live exchange API integration, strategy backtesting, real-time data ingestion, signal execution pipelines Deployment and Infrastructure: Docker, Vercel, GCP, AWS, Railway, async pipelines LLM Evaluation and Observability: eval harnesses, accuracy benchmarking, prompt testing, production monitoring, LangSmith

6+ Years of Full-Time Experience as a Software Engineer. Hands-on experience delivering Startup to Enterprise-grade AI and Software Engineering solutions across multiple industries. Specialized in building AI Agents, RAG applications, LLM-powered automation, workflow orchestration, and business process automation. Experienced in Frontend, Backend, APIs, Databases, Vector Databases, Prompt Engineering, and AI application deployment. Helping businesses automate repetitive workflows, build intelligent assistants, enterprise chatbots, internal knowledge systems, and AI-powered productivity tools. Languages: Python, Java, JavaScript, SQL. AI & LLM: OpenAI, Anthropic Claude, Gemini, Llama, Mistral, Hugging Face Transformers, LangChain, LangGraph, MCP (Model Context Protocol), AI Agents, Agentic AI, RAG, Prompt Engineering, Structured Output, Function Calling. Fine-tuning: LoRA, QLoRA, PEFT, Hugging Face, Unsloth, Model Evaluation, Dataset Preparation. Vector Databases: ChromaDB, FAISS, Pinecone, Qdrant, Weaviate. Backend & APIs: FastAPI, REST APIs, WebSockets, Pydantic, PostgreSQL, MongoDB, SQLite, Redis. LLMOps & Deployment: Docker, Git, GitHub, GitHub Copilot, LM Studio, Ollama, vLLM, CI/CD, AWS, GCP, Azure. Evaluation & Observability: LangSmith, Ragas, DeepEval, Promptfoo, Logging, Monitoring. Support: AI solution consulting, enterprise chatbot development, RAG implementation, workflow automation, debugging, production issue fixing, code reviews, project setup, mentoring, and freelance support.

𝗜 𝗯𝘂𝗶𝗹𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 — 𝗻𝗼𝘁 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲𝘀. Most AI projects look great in a demo and break when they meet real work. The agent routes to the wrong tool. The RAG retrieves the wrong chunks. The voice latency kills the conversation. The pipeline runs in a notebook but never makes it to production. That's where I come in. In 2 months at a Dubai tech company, I built and deployed 6 production AI services from zero infrastructure. Voice assistants, document tools, RAG pipelines, agent workflows — all live, all on AWS, all monitored with LangSmith. ─────────────────────────── WHAT I'VE SHIPPED ─────────────────────────── ◆ Voice assistant with 7-agent architecture — real-time speech (Deepgram + LiveKit), Text-to-SQL, RAG document Q&A, multi-agent routing via LangGraph ◆ AI Status Reports — optimized 17s → 2.1s, beat the 5-second requirement by 58% ◆ Document Classifier — bulk upload with Gemini 2.0 Flash. $0.0002/doc, 100% accuracy in production testing ◆ COSTRA — 5-agent BOQ extraction (internal tool) — vision models (Claude 4.5 Sonnet, Gemini 3 Pro) extracting data from construction PDFs, exports to Excel/JSON ◆ Medical RAG (graduation research, distinction with honors) — F1: 79.3 / EM: 69.4 on LitQA v2 with hybrid retrieval, cross-encoder reranking, context compression ─────────────────────────── WHAT I BUILD FOR YOU ─────────────────────────── ◆ Multi-Agent Systems — LangGraph, LangChain, tool routing, structured outputs, conditional flows ◆ Voice AI Agents — phone calls (Telnyx), browser-based (LiveKit), real-time STT/TTS (Deepgram, ElevenLabs), Silero VAD ◆ RAG Pipelines — over your documents, databases, and APIs. Hybrid retrieval, reranking, evaluation. ◆ LLM Apps & APIs — Claude, GPT-4, Gemini, Groq, or self-hosted (vLLM, Ollama) ◆ Production Deployment — FastAPI → Docker → AWS (Lambda, EC2, Bedrock) with CI/CD, Caddy reverse proxy, LangSmith monitoring ─────────────────────────── TECH STACK ─────────────────────────── Languages & Frameworks — Python, FastAPI, Flask, Next.js, React Agents & Orchestration — LangGraph, LangChain, multi-agent systems, tool calling RAG & Retrieval — Hybrid retrieval (BM25 + dense), cross-encoder reranking, context compression, chunking strategies Voice AI — Deepgram, LiveKit, Telnyx, ElevenLabs, Silero VAD, Gemini Live API LLMs — Claude, GPT-4, Gemini, Groq, AWS Bedrock, vLLM, Ollama Vector Stores — FAISS, Pinecone, Qdrant, Milvus, Chroma, pgvector Cloud & Infra — AWS (EC2, Lambda, Bedrock, API Gateway, S3, Amplify), Docker, Coolify, CI/CD Observability — LangSmith, Langfuse Backend — PostgreSQL, Supabase, Neo4j AuraDB Automation — n8n, REST APIs, webhooks ─────────────────────────── AWS Certified Cloud Practitioner. Tell me what you're trying to build. I'll tell you honestly if it's the right approach and how I'd build it.

I build production-grade GenAI systems for Fortune 500 enterprises including Verizon and PNC, : AI agents, RAG platforms, semantic layers, knowledge graphs, text-to-SQL/NLQ systems, and full-stack AI applications. I have 9+ years of experience across machine learning, computer vision, NLP, backend engineering, and full-stack product development. Recently, I have led large engineering teams building enterprise AI platforms for telecom and banking use cases. What I can help you build: • AI agent systems using LangGraph, LangChain, MCP, A2A, FastAPI, and custom tool orchestration • RAG applications over documents, databases, knowledge bases, and enterprise data • Semantic layers and ontology-driven systems for analytics and GenAI use cases • Text-to-SQL / natural language database querying over Postgres, BigQuery, and Teradata • Knowledge graph RAG using Neo4j, vector databases, metadata extraction, and domain modeling • Full-stack AI SaaS products with Python, React, Next.js, Docker, Kubernetes, and cloud deployment • LLM fine-tuning, evaluation, prompt engineering, retrieval evaluation, and hallucination reduction Relevant achievements: • Led a 14-member team to build an asynchronous, extensible, A2A-compliant agentic framework for enterprise troubleshooting. • Reduced ticket resolution time in a production troubleshooting workflow from days to minutes. • Built and deployed hundreds of tools across multiple MCP servers. • Led a 12-member team to build a natural language query platform for enterprise databases. • Improved NLQ accuracy using domain modeling, metadata generation, and knowledge graph traversal. • Built an ontology-powered Knowledge Graph RAG platform over unstructured enterprise data. • Co-founded and scaled an AI video translation/dubbing product from $0 to $100k revenue in 6 months. • Built computer vision systems deployed across industrial locations in India and Europe. Tech stack: Python, FastAPI, LangChain, LangGraph, LlamaIndex, OpenAI, HuggingFace, PyTorch, PEFT/LoRA, React, Next.js, Postgres, BigQuery, Neo4j, Pinecone, Docker, Kubernetes, ArgoCD, GCP, AWS, PySpark, Airflow. I am best suited for clients who need more than a prototype. I can help you go from ambiguous AI idea → system design → implementation → deployment → evaluation → production hardening.

Last project: a multi-agent system that handles end-to-end case evaluation for a legal client, replacing what used to be hours of manual review. That's the kind of problem I like — turning a messy, manual workflow into a reliable automated system. I'm a Senior AI Engineer (5+ years) specializing in RAG pipelines, multi-agent architectures, and production LLM systems — from whiteboard to AWS deployment. Recent work: Built a multi-agent legal AI system — a conversational intake agent that evaluates cases and recommends outcomes in real time Designed a RAG pipeline over a dense regulatory corpus, cutting document retrieval time by 60%+ Built an e-commerce shopping agent (Amazon/eBay) using LLM-based intent classification — replacing hardcoded category logic with a dynamic query mapper Shipped scalable AI APIs handling thousands of daily requests at sub-200ms latency What I bring beyond code: I scope projects like a product engineer first — defining what "done" actually means (POC vs. production-ready) before writing a line of code. That means clearer expectations, fewer pivots, and timelines that hold. Stack: Languages: Python, Node.js LLM frameworks: LangChain, LlamaIndex, OpenAI, Claude, HuggingFace, Ollama Vector DBs: Pinecone, Weaviate, ChromaDB, pgvector Agent frameworks: AutoGen, CrewAI, LangGraph Cloud & MLOps: AWS (SageMaker, Bedrock, Lambda, S3, EC2), Docker, FastAPI Databases: PostgreSQL, MongoDB, Redis Search: Elasticsearch, FAISS Bonus: I also work fluently across English, French, and Arabic — useful if your project involves multilingual content, MENA markets, or non-English document corpora. I work autonomously and communicate proactively — you'll know exactly where a project stands at every stage, with no surprises near a deadline. If you need an AI engineer who can own both technical architecture and product judgment — let's talk.

⭐⭐⭐⭐⭐ Senior AI Engineer | Production 𝐑𝐀𝐆, 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡 | 𝐏𝐲𝐭𝐡𝐨𝐧, 𝐀𝐖𝐒, 𝐂𝐥𝐚𝐮𝐝𝐞 I'm a AI Systems Engineer and Senior Python Backend Engineer with 8+ years of backend development experience, specializing in LangGraph, advanced multi-agent workflows, and production-grade Agentic RAG. I help business unlock the value of their private data by building custom AI systems that are accurate, secure, resilient, and production-ready. Unlike generic ChatGPT wrappers, my solutions ground frontier LLMs like Anthropic Claude and OpenAI in your own documents and databases - eliminating hallucinations, enabling complex agentic reasoning, and ensuring answers are traceable to source material. I've delivered robust RAG pipelines for legal tech, healthcare, e-commerce, and internal enterprise knowledge bases. 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝 - 𝐄𝐧𝐝‑𝐭𝐨‑𝐄𝐧𝐝 𝐑𝐀𝐆 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 ➤ Core stack: Python + LangGraph / LangChain / LlamaIndex + Vector DB (Pinecone, ChromaDB, FAISS) + OpenAI / Claude + AWS (Bedrock, Lambda, S3, ECS) ➤ Typical project scope includes: ○ Multi-Agent Workflows: Build complex, stateful, and cyclical LLM applications using LangGraph for advanced decision-making and tool use. ○ Ingestion pipeline: Parse PDFs, Word docs, HTML, markdown, Slack exports, Confluence, Notion, or database dumps. ○ Smart chunking & embedding: Semantic chunking, overlap strategies, and embedding with text-embedding-3, Cohere, or open‑source BGE models. ○ Hybrid retrieval: Combine vector similarity (dense) with BM25 keyword search (sparse) + cross‑encoder re‑ranking for maximum precision. ○ Generation with source grounding: Prompt engineering to force citations, confidence scores, or fallback responses when data is insufficient. ○ Conversational memory: Chat history summarization, session management, and multi‑turn RAG for assistants that remember context. ○ Caching & optimization: Semantic caching (GPTCache), embedding caching, and async retrieval to reduce latency and token costs. ○ Evaluation & observability: RAGAS, TruLens, or custom metrics - context relevancy, answer faithfulness, recall, and latency tracking. 𝐀𝐈 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫 e & 𝐀𝐖𝐒 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 - 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐑𝐞𝐚𝐝𝐲 ➤ I don't just write scripts - I build secure, scalable, serverless or containerized backends. Every AI system I deliver is: ○ Containerized with Docker - Reproducible environments. ○ Deployed on AWS - Using API Gateway + Lambda (serverless inference), ECS/Fargate (for long‑running jobs), S3 (document storage), RDS/DynamoDB (metadata), and Bedrock (for enterprise-grade cloud LLMs like Claude). ○ Secure by design - API keys managed via Secrets Manager, IAM roles, VPC isolation, and rate limiting. ○ CI/CD ready - GitHub Actions or GitLab CI for automated testing and deployment. ○ Monitored - CloudWatch logs, X‑Ray tracing, and custom dashboards for usage, latency, and cost tracking. ➤ Example architectures I've delivered: ○ Agentic Customer Support System - Built with LangGraph, utilizing custom tool-calling for CRM integration and automated ticket resolution. ○ Chat‑over‑PDFs for a law firm - 10,000+ documents, sub‑second retrieval, Claude 3.5 Sonnet + Pinecone serverless on AWS Lambda. ○ Semantic product search for an e‑commerce store - hybrid search (FAISS + BM25) with re‑ranking, deployed on ECS. ○ Internal company FAQ bot - Slack‑integrated, using ChromaDB and OpenAI, with conversation memory and admin feedback loop. 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐓𝐨𝐨𝐥𝐛𝐨𝐱 (𝐧𝐨 𝐭𝐚𝐛𝐥𝐞𝐬 - 𝐜𝐥𝐞𝐚𝐧 𝐥𝐢𝐬𝐭𝐬) ○ Languages & Frameworks: Python (advanced) | TypeScript (basic) | FastAPI | Django REST | Flask | GraphQL ○ LLM & RAG Frameworks: LangGraph | LangChain | LlamaIndex ○ Vector Databases: Pinecone | ChromaDB | FAISS | Qdrant | Weaviate | Milvus ○ LLM Providers & Models: Anthropic Claude | OpenAI | Google Gemini | Groq | Together.ai | LLaMA 3 | Mistral | Mixtral | Zephyr (via Ollama, vLLM, HuggingFace) ○ Embedding Models: OpenAI Ada | text‑embedding‑3‑small/large | Cohere | Voyage | BGE | Instructor ○ AWS Services (primary cloud): Lambda | API Gateway | ECS/Fargate | S3 | Bedrock | RDS (PostgreSQL with pgvector) | DynamoDB | Secrets Manager | CloudFront | CloudWatch ○ Other Cloud Options: Google Cloud (Vertex AI, Cloud Run) | Azure (AI Search, OpenAI) ○ Databases & Caching: PostgreSQL (pgvector) | DynamoDB | Redis | GPTCache ○ DevOps & Testing: Docker | GitHub Actions | Terraform (basic) | pytest | RAGAS | TruLens | DeepEval | LangSmith ○ UI for Demos: Streamlit | Gradio | Chainlit 𝐋𝐞𝐭'𝐬 𝐖𝐨𝐫𝐤 𝐓𝐨𝐠𝐞𝐭𝐡𝐞𝐫 If you need a reliable, backend-first, AWS‑savvy AI Systems Engineer who writes clean Python and delivers real business value, let's talk.