Hire the Best Model Deployment Specialists

As an AWS Advanced Tier Partner and Claude Partner, we help startups and enterprises design, build, deploy, and scale reliable AI solutions. ✅ AWS Advanced Tier Partner ✅ Claude Partner ✅ Enterprise AI Specialists ✅ Production-Ready AI Solutions ✅ Secure & Scalable Architecture ✅ Strong AI + Cloud Engineering Team ---------------------------------- Common Use Cases ---------------------------------- Automating legacy business workflows using GenAI and AI Agents Designing and building custom MCP (Model Context Protocol) servers Connecting legacy databases to MCP servers Building custom MCP tools for enterprise applications Connecting CRMs, ERPs, APIs, and internal systems to AI Developing AI agents that can read, reason, and take actions Building multi-agent systems for complex business workflows Creating enterprise AI copilots for employees Building customer support AI assistants Developing Retrieval-Augmented Generation (RAG) applications Integrating AI with existing SaaS products Automating document processing, extraction, and classification Building AI-powered search across enterprise knowledge Creating voice AI agents and AI calling systems Modernizing enterprise applications with Amazon Bedrock, Claude, and OpenAI Building secure AI APIs and backend services Deploying scalable AI solutions on AWS ------------------------- Expertise ------------------------- AI Workflow Automation Automating legacy workflows using GenAI AI-powered business process automation Intelligent workflow orchestration Human-in-the-loop AI systems Agentic AI Production-ready AI Agents Multi-Agent Architectures AI Copilots Autonomous task execution Tool-using AI Agents Planning and reasoning workflows MCP (Model Context Protocol) Designing and building MCP Servers Building custom MCP Tools Connecting legacy databases to MCP Connecting CRMs, ERPs, APIs, and enterprise systems through MCP Secure enterprise MCP deployments Enterprise AI Integration Internal AI assistants Customer support AI Enterprise knowledge assistants AI integrated into existing applications RAG (Retrieval-Augmented Generation) Document Intelligence Intelligent Document Processing Contract extraction Invoice processing Medical document analysis Legal document processing PDF understanding Document classification and indexing Reliable AI Engineering AI Evaluation frameworks Prompt Engineering AI Guardrails AI Testing Reliable AI system design Continuous AI improvement AI Observability AI tracing Agent monitoring Workflow debugging Performance monitoring Production observability Cost optimization Production Deployment Amazon Bedrock implementations Claude integration OpenAI integration AWS deployment Scalable cloud architecture Production AI infrastructure Technologies Model Context Protocol (MCP) Claude Amazon Bedrock OpenAI Gemini LangGraph LangChain LangSmith CrewAI Python FastAPI Node.js AWS Lambda ECS EKS Docker Kubernetes Terraform Pinecone PostgreSQL MongoDB Redis n8n Whether you need to automate legacy workflows, build MCP servers, design Agentic AI systems, integrate AI into enterprise software, or deploy production-grade AI on AWS, we can help turn your AI vision into a reliable, scalable solution. Let's build AI that doesn't just answer questions—it gets work done.

✅ Skilled Machine Learning Engineer with experience in applying AI to behavioral science, vision analysics, , agriculture, and computer vision, including the latest generative AI solutions. Focused on improving tools to maximize productivity, reduce latency, and adapt to business needs, with expertise in full Mlops implementation. If you are looking for guidance or complete project under the below domains. ➡️ Generative AI for your purpose: Can enhance, search do literatures review for better generative model for your use whether from text/video/audio or multimodal domains. ✅ Experiment different models from hugging face with better prompt engineering techniques and benchmark them ✅ Finetune using Lora, Qlora, and other complex architectures for resolving your problem on custom dataset ✅ For image generation finetune Stable diffusion using Dreambooth for better understanding. Comfy UI and replicate for easier flow of image generation pipeline ➡️ LLM: Deepseek, gpt, claude, llama, including pretraining, finetuning, prompt engineering, RAG approaches ➡️ Multimodal LLM: LLama 3.2, Multimodal RAG, LLAVA for vision task, Florence ➡️ Video Analysics: Merging of different deep learning and transformer based architecture for understanding of videos of sports, scenes, advertisements, action analysics etc ➡️ AI Agents: Agentic AI to solve the time taking work, CrewAI ➡️ AI workflow and Automations: No code automations tools like gumloop, make.com and N8N. Triggering specific events to provide you a new assistant. ➡️ Computer Vision: Text to image models, Object detection, and facial recognition etc. I am proficient in OpenCV, TensorFlow, PyTorch, and other. Amazon Rekognition. Transformer based (CLIP, SAM, YOLO World) ➡️ Natural Language Processing (NLP): Large Language models and transformers , AI chatbot, ChatGPT, BERT, SQUAD. Pretraining, Fine tuning, RAG based, prompt engineering ➡️ Data Science: I am proficient in data analysis, data visualization, and machine learning. I have experience in building predictive models, clustering algorithms, and recommender systems ➡️ AI Projects: Any AI, machine learning or deep learning project you have in mind Projects under the domain of Agri tech, finance, stocks exchange, business, manufacturing, transportation and health care. ❇️ Libraries tools: Pandas, Matplotlib, numpy, tensorflow, OpenCV, Langchain, NLTK, spacy, Pytorch, sklearn, FastAPI, mlflow, DVC, dagshub, Github CICD, Docker, langchain, hugging face, comfy UI, Replicate ❇️ Cloud deployment: Runpod, Heroku, Azure Devops AWS, Sagemaker, Azure , server less gpus architecture and more

🚨 Most AI integrations work in demos. They break in production. I'm an AI Engineer who builds reliable AI systems. My specialty is the layer where most projects fail: agent orchestration that survives real edge cases, RAG that retrieves accurately under load, and models that are measured, not just demoed. And I go deeper than most: real ML - LLM fine-tuning (LoRA/PEFT, 8B→70B on A100/H100), evaluation harnesses, and MCP-based agent tooling. That means production AI that's grounded, observable, and reversible. Background: Contracted as Principal AI Product Architect for a UK SaaS company — architected the automation engine that saved clients $12K/month and replaced 2–3 FTEs of manual work. Top Rated on Upwork with 100% Job Success. ------------------------------------ ➤ WHAT I BUILD ------------------------------------ ✔ Agentic Systems & Multi-Agent Orchestration LangGraph · CrewAI · AutoGen · MCP | tool use, memory, state, human-in-the-loop → Deployed agents replacing 2–3 FTEs of repetitive operations ✔ Production RAG (Grounded, Not "PDF Chatbots") Pinecone · FAISS · Milvus · pgvector | hybrid retrieval, reranking, citations, eval gates, "I don't know" when sources are silent → Cut support tickets 40% for a SaaS client ✔ MCP & Claude Code Engineering MCP servers/integrations · Claude Code workflows · function calling · tool contracts → Agent tooling that's reliable and testable ✔ LLM Fine-Tuning & MLOps LoRA/PEFT · TRL · quantization · vLLM-style serving · eval harnesses · CI/CD → Domain LoRA on DeepSeek-R1-Distill-Llama, 8B→70B, packaged adapters ✔ AI Voice Agents Vapi · Retell · ElevenLabs · Twilio | inbound/outbound, booking, routing → Human-sounding agents wired to calendars + CRM ✔ n8n / Workflow Automation Multi-step logic · CRM · lead pipelines · AI-triggered flows → 25+ hrs/week saved per client · 35% lead conversion lift ------------------------------------ ➤ MY STACK ------------------------------------ - Agents: LangChain · LangGraph · CrewAI · AutoGen · MCP · Claude Code - Models: OpenAI · Anthropic Claude · DeepSeek · open-source LLMs - Fine-Tuning: LoRA/PEFT · TRL · bitsandbytes · quantization · eval harnesses - RAG: Pinecone · FAISS · Milvus · PostgreSQL + pgvector · hybrid + rerank - Voice: Vapi · Retell · ElevenLabs · Twilio · Whisper - Automation: n8n · Make · Zapier · Webhooks · REST APIs - Backend: Python · FastAPI · Node.js - Cloud/MLOps: AWS (Certified) · GCP · Docker · Kubernetes · CI/CD · AWS Bedrock ------------------------------------ ➤ BEST FIT FOR ------------------------------------ → SaaS founders embedding AI agents into their product → Teams needing grounded, cited RAG (legal/finance/regulated) → Companies needing MCP / Claude Code agent engineering → Businesses deploying AI voice agents for calls & bookings → Anyone who needs AI that survives real usage ------------------------------------ Not sure what to build first? Send me your workflow or use case - I'll map 2–3 high-impact AI wins for free, then you decide. I review architecture before writing a single line of code. If you want AI that works when users depend on it - click Invite and describe the problem

I help startups and businesses design, build, and operate scalable, production-grade backend systems and cloud-native applications, with a strong focus on AI-powered solutions. I am an AWS Certified CloudOps Engineer with a solid foundation in cloud engineering, backend development, and system reliability. I specialize in building high-performance services and integrating AI/ML capabilities into real-world applications, ensuring they are scalable, secure, and optimized for production environments. My experience includes developing and deploying RESTful APIs, AI-driven services (including LLM integrations and retrieval-based systems), and distributed architectures using Python, FastAPI, Docker, and AWS. I apply best practices in cloud architecture, automation, monitoring, and performance optimization to deliver systems that are reliable, maintainable, and cost-efficient. Core areas of expertise: Backend development and API design (Python, FastAPI, microservices) AI/ML integration (LLMs, RAG systems, data pipelines) Cloud engineering on AWS (deployment, scaling, optimization) Containerization and DevOps (Docker, CI/CD, automation) System reliability, monitoring, and performance tuning I focus on delivering clean, well-architected solutions with clear communication and a strong commitment to quality, reliability, and long-term value.

I am a machine learning engineer with strong experience in natural language processing, audio analytics, and generative models. My work covers text clustering and classification, token classification, generative text, agentic workflows, and practical applications of large language models. I also have hands-on experience with automatic speech recognition, audio classification, conversational analytics, diffusion models, autoencoders, and fine-tuning LLMs. I build primarily with Python, PyTorch, and Hugging Face Transformers. On the MLOps/DevOps side, I deploy and operate services with FastAPI, Docker, and Kubernetes, and serve inference using NVIDIA Triton, vLLM, and TGI. I have expirience working with AWS Bedrock Agents and AgentCore, Lambda, ECR/ECS, and related services and have used CI/CD with GitHub Actions, GitOps practices and ArgoCD to streamline MLOps practices.

Hi, I’m Mehul, a Top-Rated-Plus AWS Expert and DevOps Engineer who helps startups and founders launch and scale reliable SaaS, healthcare, and fintech platforms on AWS. ✅ Top 1% Upwork Freelancer ✅ 125+ Projects Completed ✅ 7000+ Hours Worked ✅ Top-Rated Plus on Upwork ✅ AWS Certified Developer - Associate 🏆 🏆 I’ve designed systems serving 2M+ users, built HIPAA-compliant healthcare apps, and helped teams cut cloud bills by 30–40 % without compromising performance. My focus is simple: build it once, make it scale, and keep your costs under control. ✅ What I Do ➥ Architect secure, serverless AWS setups using Lambda, API Gateway, and DynamoDB ➥ Deploy AI Agent to AWS with Bedrock ➥ Automate deployments with Terraform, GitHub Actions, and CodePipeline ➥ Optimise infrastructure for speed, uptime, and cost 💡 Why Work With Me ➥ 7+ years in AWS DevOps ➥ $300 K+ earned on Upwork, 100 % Job Success Known for clear communication, quick delivery, and long-term reliability Let’s connect if you’re building a product and want an AWS setup that’s secure, scalable, and easy to manage so you can focus on growing your business while I handle the cloud. Experienced with Amazon EC2, S3, RDS, Aurora, DynamoDB, ElastiCache, CloudFront, API Gateway, Step Functions, EKS, Fargate, CodePipeline, CodeBuild, CloudWatch, Grafana, Prometheus, GuardDuty, Security Hub, AWS Organisations, SCPs, KMS, Secrets Manager, IAM, WAF, Route53, ACM, Amplify, SageMaker, Bedrock, OpenSearch, and CloudFormation. Python, Bash, TypeScript, Node.js, Docker, Kubernetes, GitHub Actions, GitLab CI, Jenkins, n8n, Zapier.