Hire the Best Common Language Runtime Specialists

Availability: Full-time freelancer, 𝟰𝟬+ hours/week, open to long-term collaborations. I’m a Full-Stack & AI Engineer with 10+ years of experience building web and mobile applications and 3+ years of specialized experience in AI and Large Language Models (LLMs). I design, develop, and deploy production-grade platforms, from scalable SaaS dashboards to AI-powered assistants, RAG systems, and voice agents. I work end-to-end: architecture → backend → frontend → cloud deployment, with a focus on clean code, maintainable systems, and high performance. Over the past few years, I’ve delivered solutions that integrate AI/LLM pipelines, vector search, real-time chat, and voice agents for enterprise and startup clients. 🤖 AI & LLM Expertise - MCP Server Development: Designing and integrating custom MCP servers for AI agents, enabling structured tool usage, external system integrations, database querying, and API orchestration. - Fine-Tuning: Persona creation, Q&A systems, and domain-specific models (medical, legal) using Mistral and Llama 3. - Synthetic Dataset Generation: Streamlining LLM training with high-quality datasets. - Evaluation Frameworks: Assessing LLM performance with custom metrics. - Cloud Deployment: Deploying LLMs on AWS and GCP. - AI Agents & Voice Bots: Proficient with LiveKit, Retail AI, OpenAI. - Open-Source Deployment: Expertise deploying models like vLLM on AWS/GCP/RunPod using SkyPilot. 🛠️ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 ➜ LLM Tools: LangChain, Langsmith, Langfuse , Hugging Face, Transformers. ➜ Vector Databases: Chroma, FAISS, Pinecone, Qdrant , Opensearch ➜ AI Workflows: Flowise AI, LangFlow, StackAI. 🛠️ 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 ➜ Languages & Frameworks: Python, Node.js, ReactJS. ➜ Database Management: MongoDB, MySQL, PostgreSQL , Supabase , FIrebase ➜ Frontend & Backend Integration: Seamlessly connecting APIs and user interfaces. 🌟 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 ➜ Open-Source LLMs: Proficiency in LLAMA 3, Mistral 7B, and Mixtral 8x7B. ➜ Prompt Engineering: Expertise in techniques like Chain of Thought, Few-shot Prompting, and Self-Reflection. ➜ Fast Inference: Implementing high-speed solutions with vLLM . 🌟 𝗪𝗵𝘆 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲? With over 10 years of experience, I deliver scalable, cutting-edge solutions tailored to your project’s needs. Whether it's advanced AI models, MCP server development, LLM optimization, or full-stack development, I ensure top-notch results every time. Let’s collaborate to bring your ideas to life!

Most AI developers can build a prototype. Very few can optimize and deploy multimodal LLMs on Jetson Orin Nano at real-time speeds, architect enterprise-grade RAG systems across 3,000+ SQL tables, fine-tune state-of-the-art vision-language models, or serve high-throughput inference with vLLM and SGLang at production scale. That’s what I do. I’m a Senior AI/ML Engineer specializing in: • Production LLM Systems & Agentic AI • Edge AI & High-Performance Model Optimization • Computer Vision & Real-Time Multimodal AI • Distributed Inference & AI Infrastructure I don’t just wrap APIs I engineer AI systems from the model architecture and optimization layer all the way to scalable production deployment. What makes my work different: → I build multi-agent AI systems that feel invisible to users. Designed a dual-LLM tutoring architecture where a speech-to-speech AI tutor interacts live with students while a hidden orchestration LLM performs real-time prompt injection, memory routing, retrieval planning, and response control completely transparently. → I optimize and deploy AI where most teams fail. Fine-tuned PaliGemma, converted it to ONNX, applied INT8/FP16 quantization and TensorRT optimization, reducing model size by 65% with <2% accuracy loss, then deployed it on Jetson Orin Nano, Raspberry Pi, edge devices, and mobile hardware for real-time inference with extremely limited compute. → I build enterprise RAG systems that reason not just retrieve. Architected a multi-stage multilingual RAG pipeline using HyDE, recursive retrieval, schema-aware chunking, metadata filtering, hybrid search, reranking, graph-based retrieval, chain-of-table reasoning, and long-context orchestration across 3,000+ enterprise SQL tables and 150+ warehouses. → I engineer high-performance LLM inference infrastructure. Built scalable inference pipelines using vLLM, SGLang, ONNX Runtime, TensorRT-LLM, DeepSpeed, FlashAttention, speculative decoding, paged attention, KV-cache optimization, and distributed GPU serving for low-latency, high-concurrency AI systems. → I deliver video AI and lip sync systems beyond standard open-source baselines. Fine-tuned LatentSync on a 4,000-sample dataset, outperforming Wav2Lip, GAN-Wav2Lip, and Wav2LipHD baselines in multilingual real-time video translation with highly coherent visual speech synchronization. → I design AI systems for real-world production environments. Built AI backends with FastAPI, asyncio, WebSockets, Redis queues, Kafka streaming, GPU worker orchestration, autoscaling inference services, and Kubernetes-based deployments capable of handling real-time concurrent workloads. What I build for clients: • Production-grade RAG systems with hybrid, recursive, graph, and agentic retrieval • High-throughput LLM serving using vLLM, SGLang, TensorRT-LLM, Triton Inference Server • Edge AI deployment with ONNX, TensorRT, OpenVINO, CoreML, TensorFlow Lite, ONNX Runtime • Fine-tuning pipelines (LoRA, QLoRA, PEFT, RLHF, DPO) with evaluation and benchmarking • AI agents with LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, MCP architectures • Multimodal AI systems combining vision, audio, speech, and language models • Text-to-SQL systems for complex enterprise schemas with multilingual querying • Computer Vision pipelines YOLOv8, YOLO11, SAM2, GroundingDINO, DETR, Mask R-CNN • Real-time speech AI Whisper, XTTS, RVC, voice cloning, speech-to-speech pipelines • Video AI lip sync, face reenactment, multilingual dubbing, avatar systems • Distributed AI infrastructure with Docker, Kubernetes, Ray, Celery, Redis, Kafka • AI observability, evaluation, tracing, and monitoring with LangSmith, MLflow, Weights & Biases • Vector databases and retrieval systems FAISS, Pinecone, ChromaDB, Milvus, Qdrant, Weaviate • AI surveillance and geospatial intelligence systems with real-time threat detection Core Stack: Python · PyTorch · TensorFlow ·  HuggingFace · Transformers · LangChain · LangGraph · LlamaIndex · OpenAI · Anthropic · Gemini · FastAPI · vLLM · SGLang · TensorRT-LLM · Triton · ONNX Runtime · TensorRT · OpenVINO · TensorFlow Lite · CUDA · OpenCV · MediaPipe · Whisper · YOLO · SAM · GroundingDINO · FAISS · Pinecone ·  Qdrant · MongoDB · PostgreSQL · Redis · Kafka ·  Docker · Kubernetes · GCP · Vertex AI · AWS · Azure · WebSockets If you need an AI engineer who can go deeper than most teams whether that means deploying optimized multimodal AI on constrained edge hardware, architecting enterprise RAG systems that minimize hallucinations, building scalable LLM inference infrastructure, or shipping production-grade multi-agent AI send me a message. I’ll tell you honestly what’s possible, what’s overhyped, and exactly how I’d build it.

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. ✅ You are a good fit if: You need a chatbot, voice agent, RAG system, trading automation, or web product built to work in production. You have a clear use case and want clean architecture, not just a demo. You want one engineer who handles the AI, backend, and frontend without handoffs. ⚙️ 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

Top Rated Freelancer | 100% Job Success Score | $30K+ Earned | 1,900+ Hours Worked Are you looking for a reliable AI Trainer, LLM Evaluator, Data Annotation Specialist, or Multilingual Language Expert who can deliver high-quality work with accuracy and consistency? I help AI companies, startups, research organizations, and localization agencies improve their AI models through data annotation, prompt evaluation, chatbot training, fact-checking, search evaluation, language localization, and translation services. Over the past 6+ years, I have worked on large-scale AI and language projects involving Generative AI, LLM evaluation, chatbot training, prompt assessment, data labeling, content moderation, search quality evaluation, localization, and multilingual linguistic tasks. My experience includes working as: ✓ AI Chatbot Trainer ✓ AI Evaluator & Prompt Reviewer ✓ Data Annotation & Data Labeling Specialist ✓ Search Quality Evaluator ✓ QA Reviewer ✓ Hindi Localization Expert ✓ Lead Hindi Translator I have contributed to projects involving conversational AI, voice AI systems, multilingual datasets, prompt-response evaluation, content quality assessment, model alignment, fact-checking, and language optimization. Notable experience includes: • Lead Hindi Translator for MedAssent • AI Chatbot Trainer at Gethybrid • QA and language evaluation responsibilities on large-scale AI projects • Voice AI and agent evaluation work for Cartesia-related projects • Search evaluation and quality rating projects • Localization and translation projects across Hindi, Urdu, and English Languages: ✓ Native Hindi ✓ Native Urdu ✓ Fluent English Core Services: • AI Training • LLM Evaluation • Prompt Evaluation • RLHF-Related Tasks • Data Annotation • Data Labeling • AI Fact Checking • Search Quality Rating • Sentiment Analysis • Content Moderation • Localization • Translation • Transcreation • Proofreading • QA Review Why clients hire me: ✓ Strong AI training and evaluation experience ✓ Multilingual language expertise ✓ High attention to detail ✓ Consistent quality standards ✓ Experience reviewing and auditing contributor work ✓ Excellent communication ✓ Proven Upwork track record Whether you need help training AI models, evaluating prompts and responses, annotating datasets, localizing content, or translating between Hindi, Urdu, and English, I can provide accurate, scalable, and dependable support. Let's discuss how I can contribute to your project.

Hello, I'm a Turkish Linguist with over 10 years of experience in transcription, proofreading, editing, linguistic quality assurance, and language-focused data projects, including more than 6 years supporting AI training and machine learning initiatives. My work spans AI data annotation, LLM evaluation, semantic ASR assessment, TTS evaluation, transcription QA, proofreading, editorial review, localization, voice recording, speech data collection, and multimodal dataset validation across text, audio, image, and video data. Areas of expertise include: • Turkish Proofreading & Editing • Linguistic QA & Editorial Review • AI Data Annotation & Dataset Validation • LLM Response Evaluation • Preference Ranking & Pairwise Comparison • Human vs AI Content Detection • Semantic ASR Evaluation • TTS Evaluation & Audio Quality Assessment • Voice Recording & Speech Data Collection • Pronunciation, Fluency & Naturalness Evaluation • Prompt Evaluation & Instruction Adherence Review • OCR Validation & Correction • Polygon Annotation & Computer Vision Datasets • Turkish Localization & Transcreation • Transcription & Speech Data Validation I have contributed to projects involving speech recognition systems, conversational AI, generative AI evaluation, AI dubbing workflows, automotive voice assistants, voice model training, speech dataset creation, localization projects, and large-scale language datasets. My background includes collaborations with companies such as Deepgram, Welo Data, Multilingual Connections, RWS Moravia, Appen, Surge AI, DataAnnotation.tech, iSoftStone, Cerence, and other AI and localization providers. Tools and platforms include Label Studio, Feather, Loft, UHRS, Memsource (Phrase), Google Sheets, and Microsoft Excel. Experienced with dataset validation, OCR review, polygon annotation, preference ranking, and guideline-based quality assurance workflows. I am detail-oriented, highly adaptable, and comfortable working with complex guidelines, large datasets, and quality-critical workflows. Whether you need a Turkish linguist for AI training, annotation, transcription, proofreading, editing, voice recording, speech data collection, localization, audio evaluation, or quality assurance, I'd be happy to help.

I'm an AWS Certified Solutions Architect with 3+ years building production AI for enterprises and high-ticket B2B teams. I work in two lanes — enterprise AWS RAG, and AI-powered CRM/sales automation — and both ship to real production, not localhost demos. 🏢 EXPERIENCE IN Enterprise Conversational AI: + Covestro AG (via FPT Software) — Migrated a legacy HR chatbot to a multi-agent RAG platform on AWS Bedrock Knowledge Base. Cohere V3 embeddings, Amazon ReRank, Azure OpenAI GPT-4o. Event-driven ingestion (Lambda + SQS + DLQ) auto-syncs ServiceNow hourly. + FPT Software — Contract Intelligence — Skill-based extraction framework with prompt chaining. 35% accuracy lift, hallucinations sharply reduced. AWS Bedrock multi-model gateway, SSE streaming, scales to thousands of contracts per batch. + HTI Group — Agentic Chatbot — LangGraph + LlamaIndex ReAct agents, AWS Lambda + ECS Fargate, CI/CD via AWS CDK (TypeScript), Langfuse observability, hierarchical semantic chunking with auto-merge. 🤖 EXPERIENCE IN AI Sales Automation (GoHighLevel + CRM) + Inbound Sales Qualifier & Nurture Engine — AI layer wired into GoHighLevel (pipelines, triggers, calendar, SMS/email) for a high-ticket B2B offer. + Instantly scores leads on an 8-dimension Fit × Intent rubric, generates personalized email/SMS per lead, and answers inbound replies through a grounded RAG agent that only speaks from your real knowledge base — no invented pricing. + Hot leads auto-route to a rep + booking link; everyone else drops into smart nurture. FastAPI + pgvector + webhooks with retry/DLQ so no lead is ever dropped, plus an EventLog audit trail and a daily summary. Built end-to-end in a focused ~10-day sprint. What I do best: 🧠 Multi-Agent Systems — LangGraph, LlamaIndex, ReAct, agent supervisors, tool/skill orchestration 🔍 Production RAG — hybrid search, cross-encoder reranking, hierarchical chunking, graph RAG, grounded "no-hallucination" reply agents 📈 AI Sales & CRM Automation — GoHighLevel workflows, lead scoring, personalized nurture, calendar booking, webhooks + middleware (Python/Node), Make / Zapier glue ☁️ AWS Bedrock + OpenSearch + Lambda + ECS — real cloud-native, not localhost demos 🔒 Compliance — PII redaction, Bedrock Guardrails, zero-trust, KMS, IAM least-privilege 📊 LLMOps — Langfuse / LangSmith tracing, eval pipelines, LLM-as-judge 🧰 Infrastructure as Code — AWS CDK / TypeScript, Docker, GitHub Actions Engagement models I take: ⚡ Fast fixed-price build sprint (1–2 weeks) — ship a working AI feature, e.g. a GoHighLevel qualifier + grounded reply agent, or an MCP/agent tool surface 🔍 Architecture review (1 week, fixed price — see Project Catalog) 🚀 POC → Production migration (3–6 weeks) 🤝 Long-term retainer for ongoing AI platform work (2+ months) 🔗 Let's talk — message me with your stack, your scale, and your timeline.