Hire the Best Amazon SageMaker Developers

AWS Certified Solutions Architect with 9+ years building high-traffic cloud systems - now I build AI systems that run entire workflows end to end, not chatbots. Done properly that is a 5x change in what a team can get through, not a 5% one. ** See my portfolio below for working examples with architecture diagrams. ** MY SERVICES 1. Custom AI Agent Development - Multi-agent systems that plan, use tools, call your internal APIs and carry a task through to completion instead of stopping at a suggestion. - Document and correspondence automation: ingest from email, upload or API, work out the intent, retrieve related facts from your own documents, classify, and draft the response. A person approves before anything is sent. - Planning, scheduling and routing agents: constraint-aware assignment and optimised routing, with an agent monitoring performance against plan. - Applications: claims, invoices, support escalations, compliance filings, procurement, HR casework, underwriting, contract review, dispatch and scheduling. 2. RAG Over Your Private Data - Retrieval that returns the right passage: chunking, embeddings, hybrid search, reranking, and evaluation so you know it actually works. - LLM cost and latency optimisation: caching, routing, model selection and prompt design so the system stays affordable at production volume. 3. Cloud Architecture, Cost & Security (AWS) - Full review of your AWS estate, ranked by money saved and risk removed. Around 20% typically comes off the bill from sizing and configuration tweaks alone, before any re-architecture. - AI-driven cost anomaly detection: an agent that watches spend continuously, separates a real anomaly from normal variance, explains the cause and names the fix. HOW I BUILD Every system follows the same architecture, which is why it ports to any domain: 1) ingest 2) understand intent 3) retrieve related context 4) classify 5) generate or decide 6) human approval gate 7) learn from every correction. Non-negotiables: the AI drafts and a human approves, every decision is auditable, and corrections feed back so accuracy improves over time instead of drifting. MY EXPERTISE - AI Agents: OpenAI (GPT), Anthropic (Claude), multi-agent orchestration, LangChain, RAG, intent classification, human-in-the-loop review, continuous-learning feedback loops - Back End: Python, FastAPI, REST APIs, constraint and route optimisation - Cloud & DevOps: AWS (Lambda, ECS/Fargate, RDS, S3), Docker, Terraform, CI/CD, observability and cost control WHY MY SYSTEMS SURVIVE PRODUCTION Nine years as a cloud architect on high-traffic systems, and AWS Certified Solutions Architect. Most AI projects die in the gap between the demo and the deploy. I handle both halves: the agent logic, plus the infrastructure, evals, observability and cost control that keep it working once real volume arrives. TRACK RECORD 11 Upwork contracts, 5.0 stars on every single one - the cloud-infrastructure foundation my AI systems run on. "Excellent work delivered from Akshay." / "Very good in aws and devops skills" Recent private-client systems: a legal correspondence agent and an AI field-ticket planner, each delivering roughly 5x efficiency gains. Tell me the process you want automated, or the cloud bill you want cut, and I'll tell you straight whether AI is the right tool, what it takes to build, and what it costs to run. Let's discuss your project.

Need to further optimize your LLM/GPT/RAG application performance with the support of a top AI expert, or start from scratch? 6+ Years, AWS and Microsoft Azure Certified AI/ML Specialist, I am focused on building Advanced AI applications around (GPT, Claude, Gemini, Azure OpenAI, etc.), and have compiled some of the most cutting-edge features in Agentic AI, RAG, and Data Dashboards to cater to specific domains and complex applications. My process to future-proof your business involves, 1. Setting up your data in the best possible format. 2. Optimizing your AI infrastructure for a more deterministic (yet creative) flow in the probabilistic domain of AI. 3. Developing a scalable architecture that anticipates growth. 4. Delivering cost-effective solutions and long-term support. 5. Prioritizing Data Security and Compliance at each increment. As a contributor to LangChain, LlamaIndex, LangGraph, n8n, and other supporting frameworks, I've engaged with the open-source LLM/RAG library space and continually participate in the LLM/AI community. My Expertise lies in custom AI Agents, Chatbots, Voice AI Agents, and Workflow Automations to make your business/product 10x more efficient. 🎓 Advanced proficiency in the following domains, ✦ Structured Data Query (Connect your database/drives/sharepoints with LLMs) ✦ Long-term Memory and Continual Learning (For an LLM, precise context is gold) ✦ AI Voice/Speech Agents ✦ MCP & A2A (Connect your APIs and AI Agents) ✦ Knowledge Extraction ✦ GraphRAG ✦ RAG Enhancement ✦ Prompt Engineering ✦ LLM Performance Analysis (Evaluation, Governance, and Monitoring) ✦ Model Deployment ✦ Model Fine-tuning 🔧 𝗖𝗼𝗿𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝗶𝗲𝘀 ● AWSxAzure Administrator & Solutions Architect - Prioritizing security and compliance, we have built many Azure-only AI Solutions for medium- to large-scale enterprises. ● ChatGPT/OpenAI-Based RAG Applications ● LLMOps (Architecture/Development): - Langchain/LangGraph, LlamaIndex, Haystack, AdalFlow, GraphRAG - Cohere, Ollama, Anyscale, Groq, DeepSpeed, SentenceTransformer, PyTorch - Any vector database (Pinecone, ChromaDB, Milvus, Qdrant, Weaviate, Azure Search) - AWS SageMaker, Amazon Bedrock, Amazon Q, Azure OpenAI & AI Studio, - Training & Fine-tuning: DPO, RLHF, PPO, PEFT (LoRA, QLoRA) ● Text-to-speech and Speech-to-text: OpenAI Whisper, Google TTS, ElevenLabs, Murf AI, Deepgram, Amazon Polly, Azure TTS ● Python Backend (Flask, Django, FastAPI, Streamlit, Dash, PyQT & Tkinter for GUI) ● Secondary Languages: C#, JavaScript/Typescript, Rust. ● Datastores: MongoDB, PostgreSQL, Redis, DynamoDB, Elasticsearch, Azure CosmosDB ● Linux Server Administration (Amazon Linux, Debian-based, RHEL), ● Web Servers: Apache2, Nginx ● Containerization and Orchestration: Docker, Docker Swarm, ECS, Kubernetes (Across major Cloud Vendors) ● AWS Serverless: API Gateway, Lambda, SNS, CloudWatch, CloudFront, S3, DynamoDB ● IaC: CloudFormation, Terraform, Ansible ● CI/CD: Jenkins, GitHub Actions, AWS CodeDeploy/CodeBuild, GitLab. ● CRM Integrations: Zendesk, GoHighLevel, HubSpot, Bitrix24. ● Integration and Automation: n8n, Make, Zapier, etc. ● Real-Time Data and API Integration 💼 Some mainstream industries and business domains that I have worked with, ✦ CRMs ✦ Healthcare ✦ Operations ✦ Education, Edtech ✦ Legal (Regulation, Compliance, Consultancy) ✦ Real Estate ✦ Retail & E-commerce ✦ Travelling and Hospitality If you are trying to build an MVP for an AI Project, my guide to you would be, "The best way to start is just by building on top of... whatever the best model is... Don’t worry about [cost or latency] at first. You’re just trying to validate the idea." Today, generating and structuring great ideas is easier than ever. What truly sets teams apart now is execution. I’ve developed a robust framework with my experience that enables rapid design and development of AI-first applications, turning ideas into impact with speed and precision. Let's transform customer experience, unlock data intelligence, and solve complex business problems together.

I build production-ready AI systems not throwaway demos. Backed by 40 completed Upwork jobs and 5+ years of AI/ML and software engineering experience, I help startups, SaaS companies, and product teams build reliable RAG applications, AI agents, LLM-powered APIs, and scalable Python services on AWS. Whether you need to build an AI product from scratch, productionize an existing prototype, improve the accuracy of a RAG system, reduce LLM costs and latency, or deploy models securely, I can help. I work across the complete development lifecycle: Technical discovery → system architecture → development → testing → deployment → monitoring → optimization 𝗪𝗛𝗔𝗧 𝗜 𝗖𝗔𝗡 𝗛𝗘𝗟𝗣 𝗬𝗢𝗨 𝗕𝗨𝗜𝗟𝗗 • RAG and enterprise knowledge assistants Document ingestion, chunking, embeddings, vector search, retrieval optimization, context ranking, citations, and grounded LLM responses. • AI agents and workflow automation Tool calling, multi-step reasoning, API integrations, structured outputs, validation, human approval workflows, and automated task execution. • LLM and Generative AI integrations OpenAI, Hugging Face Transformers, LangChain, LlamaIndex, PydanticAI, Ollama, llama.cpp, and MCP-based tool integrations. • Secure Python backends and APIs FastAPI, Django, Flask, REST APIs, background tasks, authentication, PostgreSQL, MySQL, MongoDB, and third-party integrations. • AWS AI and ML infrastructure Amazon Bedrock, SageMaker, Lambda, ECS, EKS, S3, RDS, OpenSearch, and scalable real-time inference architecture. • Private and local AI deployments Quantized LLMs, CPU-based inference, Dockerized model serving, privacy-focused deployments, and reduced cloud dependency. • Production deployment and DevOps Docker, Kubernetes, Terraform, GitHub Actions, CI/CD, Prometheus, Grafana, CloudWatch, automated testing, and infrastructure as code. 𝗔𝗜 𝗥𝗘𝗟𝗜𝗔𝗕𝗜𝗟𝗜𝗧𝗬 𝗔𝗡𝗗 𝗢𝗣𝗧𝗜𝗠𝗜𝗭𝗔𝗧𝗜𝗢𝗡 A working AI demo is only the beginning. I can also help with: • Structured and schema-validated LLM outputs • Hallucination reduction and retrieval-quality improvement • Guardrails, error handling, and fallback workflows • Prompt and context optimization • Model selection and evaluation • LLM cost and latency optimization • Logging, tracing, monitoring, and observability • Scalable and maintainable system architecture 𝗧𝗬𝗣𝗜𝗖𝗔𝗟 𝗘𝗡𝗚𝗔𝗚𝗘𝗠𝗘𝗡𝗧𝗦 • Building an AI MVP from an idea or specification • Converting an AI prototype into a production-ready application • Adding AI capabilities to an existing SaaS or web product • Auditing and improving an existing RAG or agent system • Developing a FastAPI backend for an AI application • Deploying AI services securely on AWS • Troubleshooting inaccurate, slow, or expensive LLM workflows 𝗪𝗛𝗔𝗧 𝗬𝗢𝗨 𝗥𝗘𝗖𝗘𝗜𝗩𝗘 Depending on the project, deliverables may include: • System architecture and implementation plan • Clean, maintainable source code • Secure APIs and database integrations • Automated tests and validation • Docker and deployment configuration • CI/CD pipelines and infrastructure code • Monitoring and error-handling setup • Technical documentation and knowledge transfer 𝗥𝗘𝗖𝗘𝗡𝗧 𝗘𝗫𝗣𝗘𝗥𝗜𝗘𝗡𝗖𝗘 My recent work includes production RAG pipelines, tool-using agentic workflows, schema-validated AI services, privacy-focused local LLM inference, AWS-hosted ML infrastructure, scalable inference APIs, and full-stack applications powered by Python and React. 𝗪𝗛𝗬 𝗖𝗟𝗜𝗘𝗡𝗧𝗦 𝗪𝗢𝗥𝗞 𝗪𝗜𝗧𝗛 𝗠𝗘 • I design the complete system not just the prompt • I understand AI, backend engineering, cloud infrastructure, and deployment • I communicate technical risks, trade-offs, and progress clearly • I write clean, documented, and maintainable code • I build solutions that can be monitored, improved, and maintained after launch • I focus on business outcomes instead of unnecessary technical complexity Send me a brief description of your goal, current technology stack, available data, and biggest technical challenge. I will help you identify the most practical path from idea to production.

I've architected and shipped production AI systems running across multiple countries, not demos. Among them: PropAI, a multi-tenant, Claude-powered WhatsApp AI agent platform on Meta's Cloud API serving real-estate agencies across Africa; CropSight, a GAN-based computer-vision system for precision agriculture; and an agentic financial-intelligence platform built on LangGraph + FastAPI. PhD in Machine Learning, 15 years in AI, former CTO. I help startups and enterprises turn LLM capabilities into reliable, deployable architecture (retrieval, memory, tool use, orchestration, voice, evaluation, and scalable cloud infrastructure), not prompt-engineering experiments that fall over in production. SYSTEMS I'VE SHIPPED - PropAI: multi-tenant WhatsApp AI agent platform for real-estate agencies and agents (Meta Cloud API, Embedded Signup, Coexistence). Claude-driven LLM orchestration via n8n, with RAG + agentic responses on agents' live numbers. Deployed across African markets on region-isolated, data-residency-compliant AWS; property-market feasibility studies delivered for UK clients. - Agentic financial-intelligence platform: LangGraph orchestration over structured financial data, generating personalized audio content end-to-end (LLM + TTS). - CropSight: computer-vision platform for precision agriculture and corporate-farm compliance. Fuses satellite, drone, and mobile imagery with GAN-based super-resolution mapping low-resolution sources to high-resolution field detail. Deployed across African markets. -Elena AILL (research collaboration): advising on architecture approach, technical direction, and MVP build for a from-scratch, non-transformer persistent-memory AI research project. - Enterprise AI & telematics integration for PepsiCo, plus AI architecture and code audits for international clients. WHAT I BUILD - End-to-end RAG pipelines (hybrid retrieval, reranking, vector DBs) - Agentic & multi-agent systems with tool use, memory, and orchestration - Voice AI: TTS, voice-to-voice / voice conversion, and production voice-agent pipelines - Fine-tuning and LoRA/QLoRA pipelines - Persistent-memory and continual-learning systems - Workflow automation at production scale (n8n, self-hosted) - Scalable inference infrastructure and cloud-native ML systems on AWS - CI/CD and MLOps workflows for AI products - LLM evaluation and performance-benchmarking frameworks STACK ML/DL: PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, OpenCV · LLMs: Claude, GPT, Llama, Mistral, open-source models · Orchestration: LangGraph, LangChain, LlamaIndex, n8n (production, self-hosted), custom · Voice/Speech: TTS, voice-to-voice (RVC), Whisper STT · Vector/DB: Qdrant, pgvector/Supabase, Pinecone, FAISS, BGE reranker · Backend: FastAPI · Cloud: AWS (EC2, EKS, S3, IAM, monitoring) · Deployment: Docker, Kubernetes · WhatsApp Business / Meta Cloud API Most developers build AI demos. I architect systems with clear technical decisions, cost-awareness, evaluation built in, and production-readiness from day one, and I work at the research edge of memory and continual-learning architecture, not just integration. If you're building an AI-native product and need architectural clarity, scalability strategy, and senior technical leadership, I can structure and execute it. I also do fixed-scope AI architecture and code audits, a fast, low-risk way to start.

I build production-grade Agentic AI and RAG systems for companies and startups that need intelligent automation, knowledge retrieval, and AI-powered workflows deployed at scale. My focus is on systems that go beyond simple chatbots multi-agent architectures, enterprise RAG pipelines, voice AI, analytics agents, and full-stack AI applications built to run in real business environments. 🔵CORE EXPERTISE 1. Agentic AI & RAG Systems ⚫Multi-agent systems using LangGraph, LangChain, Semantic Kernel, and OpenAI Agents SDK. ⚫Production RAG pipelines with hybrid retrieval, Cohere reranking, and validation loops. ⚫Agentic workflows with tool use, memory, planning, and multi-step reasoning. ⚫LLM observability and tracing using LangSmith. 2. Azure AI Stack ⚫Azure AI Foundry - model deployment, management, and enterprise inference. ⚫Azure OpenAI, Azure AI Search, Azure Document Intelligence. ⚫Azure Cosmos DB, Azure App Service, Docker-based cloud deployments. 3. LLMs and Models ⚫OpenAI: GPT models. ⚫Anthropic: Claude 3.5 Sonnet and Claude model family. ⚫Open-source: Mistral and LLaMA via Ollama and vLLM. ⚫Reranking: Cohere Rerank for precision retrieval 4. Full-Stack AI Development ⚫Backend: Python, FastAPI, REST APIs ⚫Frontend: React, JavaScript ⚫Databases: PostgreSQL, Cosmos DB, FAISS, Pinecone, ChromaDB ⚫Vector search, document ingestion pipelines, enterprise data integration 5. ML, Data Science and Forecasting ⚫Predictive modelling, time-series forecasting using ARIMA, SARIMAX, and LSTM ⚫Classification, regression, feature engineering, and model evaluation ⚫Deep learning with TensorFlow and PyTorch - transfer learning with EfficientNet, ResNet, VGG16 ⚫Data engineering with PySpark, pandas, and scikit-learn. 🔵SYSTEMS I HAVE BUILT ⚫Enterprise RAG Chatbot - Azure AI Foundry, Claude, LangChain Multi-source document ingestion across PDFs, SharePoint, SQL, and APIs. Azure Document Intelligence for chunking, hybrid semantic and keyword retrieval with Cohere reranking, and Claude deployed via Azure AI Foundry for cited, accurate responses. Supports number of documents, deployed on Azure App Service with a React and FastAPI interface. ⚫HR Multi-Agent RAG System - LangGraph, Azure AI Foundry, FastAPI Full-stack multi-agent system with GPT-4o intent classification routing queries across four specialized agents, greeting, employee data lookup, leave application workflow, and RAG policy Q&A. Stateful multi-turn conversations with LangGraph, FAISS vector store, PostgreSQL backend, and JWT authentication. ⚫AI Data Analyst Agent - Text-to-SQL, LangGraph, Azure AI Foundry Agentic system allowing business users to query databases in plain English. GPT-4o generates schema-aware SQL, executes queries, auto-generates charts, and produces plain-English summaries. Self-correcting agent retries failed queries automatically. Outputs include CSV and PDF export. ⚫AI Voice Agent - Call Automation, Conversational AI End-to-end voice agent handling inbound and outbound calls real-time speech-to-text, conversational AI reasoning, CRM, calendar, and ticketing API integrations, and neural text-to-speech responses. Handles lead qualification and customer support autonomously with low-latency responses. 🔵 WHAT I CAN BUILD FOR YOU ⚫ RAG assistants for documents, knowledge bases, and enterprise data. ⚫ Multi-agent AI systems with tool use, memory, and complex reasoning. ⚫ AI chatbots and conversational systems for support and internal operations. ⚫ Text-to-SQL analytics agents for business intelligence and reporting. ⚫ Voice AI systems for call automation, lead handling, and customer support. ⚫ Predictive ML models for forecasting, classification, and data analysis. ⚫ Full-stack AI applications with React, FastAPI, and Azure cloud deployment. ⚫ AI integrations connecting LLMs to your existing CRMs, APIs, and databases. 🔵 WHY WORK WITH ME ⚫I work across the full AI stack from model selection and prompt engineering to retrieval architecture, agent design, backend APIs, and cloud deployment on Azure. ⚫Every system I build is production-ready, observable with LangSmith tracing, and designed with clean architecture that scales. I do not just prototype I build and deploy systems that run in real environments. ⚫If you need a RAG system, an agentic AI workflow, a voice agent, an analytics tool, or a full-stack AI application, I can design, build, and deploy it end-to-end.

I help enterprises move AI from prototype to production — solving the problems that appear when AI systems become **expensive, slow, unreliable, and difficult to scale.** With 12+ years across Software Engineering, Technical Leadership, CTO, and AI Architecture, I design production-grade AI systems, agentic workflows, enterprise RAG platforms, and AI-native SaaS products. My focus is not simply building chatbots. I solve the engineering problems behind reliable AI: • Runaway token costs • Slow agent execution • Excessive LLM calls and agent loops • Poor RAG retrieval and hallucinations • Chunking and embedding problems • Context-window and memory problems • Unreliable multi-agent systems • AI infrastructure and scaling • Observability, evaluation and cost control ━━━━━━━━━━━━━━━━━━━━━━ 🚀 AI AGENT ARCHITECTURE I design production-ready: • Multi-agent systems • Planner → Supervisor → Worker architectures • Tool calling and API orchestration • MCP-based systems • Agent memory and state management • Human-in-the-loop workflows • Long-running/background agents • Event-driven agents • Agent evaluation, guardrails and failure recovery Technologies: Claude, OpenAI, AWS Bedrock, AgentCore, LangGraph, LangChain, MCP, n8n, Python, TypeScript, FastAPI, Node.js. ━━━━━━━━━━━━━━━━━━━━━━ 💰 AI TOKEN COST OPTIMIZATION AI systems can become extremely expensive at production scale. I reduce unnecessary: • Prompt/context tokens • Conversation history • RAG context • Tool outputs • Agent-to-agent communication • LLM calls and reasoning loops Using context compression, prompt caching, model routing, retrieval optimization, structured outputs, summarization and intelligent model selection. The goal isn't simply **"use a cheaper model."** It is to reduce inference cost while maintaining quality and reliability. ━━━━━━━━━━━━━━━━━━━━━━ ⚡ AGENT LATENCY OPTIMIZATION Slow agents are often an architecture problem. I analyze: User → API → Agent → LLM → Retrieval → Tools → APIs → Database → Response And optimize: • Sequential → parallel execution • Unnecessary LLM calls • Tool-call chains • RAG/vector-search latency • Database/API bottlenecks • Network round trips • Model selection • Streaming, caching and async execution ━━━━━━━━━━━━━━━━━━━━━━ 🧠 CONTEXT ENGINEERING Many AI systems fail because the model receives the **wrong context**. I design architectures for: • Short/long-term memory • Conversation history • Retrieved knowledge • Task and agent state • Tool results • Dynamic context • Context compression and prioritization The key questions are: **What should the model know? When should it know it? How much does it actually need?** ━━━━━━━━━━━━━━━━━━━━━━ 🔎 ENTERPRISE RAG & RETRIEVAL I build RAG systems optimized for **accuracy, latency, cost, security and traceability.** I solve: • Poor retrieval and irrelevant chunks • Hallucinations • Large context windows • Slow vector search • Metadata filtering • Multi-tenant / permission-aware retrieval • Citations and provenance • Embedding and reranking problems Typical pipeline: Document → Parsing → Chunking → Embedding → Hybrid Search → Metadata Filter → Reranking → Context → LLM Technologies: Pinecone, OpenSearch, Elasticsearch, pgvector, Weaviate, FAISS. ━━━━━━━━━━━━━━━━━━━━━━ ✂️ CHUNKING & EMBEDDING OPTIMIZATION Chunking is often a hidden cause of poor RAG performance. I work with: • Fixed, sentence, paragraph and semantic chunking • Parent-child retrieval • Document/code-aware chunking • Metadata enrichment • Overlap optimization • Embedding selection • Top-K tuning • Reranking The objective isn't smaller chunks — it's creating retrieval units that preserve the meaning needed to answer the question. ━━━━━━━━━━━━━━━━━━━━━━ 🏗️ AI AGENT INFRASTRUCTURE I design infrastructure for reliable, scalable agent execution: • Agent runtime and background jobs • Queues and state/checkpointing • Retries and idempotency • Concurrency and rate limiting • Observability and tracing • Cost monitoring • Multi-tenancy and security • Failure recovery AWS: Bedrock, AgentCore, Lambda, ECS, EKS, API Gateway, ALB, SQS, EventBridge, Step Functions, DynamoDB, Aurora, S3, OpenSearch, CloudWatch. ━━━━━━━━━━━━━━━━━━━━━━ 🤖 CLAUDE CODE / AI-NATIVE ENGINEERING I use **Claude Code as an AI engineering layer**, not just a code generator. Capabilities include: • Large codebase understanding • Architecture analysis • Refactoring and modernization • Feature implementation • Debugging and test generation • Code review • Repository intelligence • MCP integrations • Engineering automation I help teams evolve from traditional development toward **AI-native software engineering.** ━━━━━━━━━━━━━━━━━━━━━━ 🔄 AI APPLICATION MODERNIZATION I help existing SaaS and enterprise applications become AI-native: **Legacy Application → APIs → AI Layer → Agents → Knowledge → Automation** Including AI copilots,