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.

AI systems usually fail at the boundaries between models, data, APIs and real users. I help teams turn AI/ML prototypes, fragmented data pipelines and manual processes into reliable production systems. $20K+ earned across 22 Upwork jobs | 7+ years in AI, machine learning, data and cloud engineering What I can build and improve: • Production RAG systems with ingestion, hybrid retrieval, reranking, citations, grounding checks, evaluation and observability • LangGraph and agentic workflows with persistent state, structured outputs, tool calling, human approval, retries and controlled failure handling • Secure MCP servers and API integrations with authentication, least-privilege access, validation and audit logging • ETL/ELT and ML data pipelines using Python, SQL, BigQuery, PySpark, Airflow, Snowflake, Databricks and cloud-managed services • Machine-learning solutions for forecasting, classification, NLP, computer vision, feature engineering, model serving and monitoring • Python and FastAPI backends, REST APIs, Docker, CI/CD and Kubernetes • Production deployment across GCP and AWS using Cloud Run, Vertex AI, BigQuery, Dataflow and Amazon Bedrock • Operational dashboards and client-facing AI interfaces that expose quality, latency, cost and failure signals My public portfolio contains tested implementations for RAG evaluation, LangGraph orchestration, secure MCP access, AWS Bedrock deployment and AI operations monitoring. My commercial experience includes retail forecasting, hierarchical ML systems, PySpark feature pipelines, TensorFlow training and inference, semantic search, Cloud SQL, Dataproc and multi-environment delivery. I start by understanding your business outcome, current architecture, data constraints, failure cases and definition of done. You receive transparent milestones, tested code, deployment guidance, observability and maintainable handover documentation. Send me your problem, current stack and expected outcome. I will recommend the smallest practical first milestone.

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 and ship production AI systems that real users depend on, not demos. RAG pipelines, multi-agent LLM apps, fine-tuned models, and multimodal/OCR extraction, deployed to run 24/7 on Kubernetes and serverless GPU. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Top-Rated Plus | 100% Job Success | 4+ years ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Enterprise-grade AI for multinational companies and startups, including HIPAA-conscious healthcare workflows. I turn complex requirements into intelligent, production-ready applications that drive measurable results. ━━━━━━━━━━━━━━━━━━━━━ WHAT I DO BEST ━━━━━━━━━━━━━━━━━━━━━ Agentic AI & Multi-Agent Systems Custom architectures with LangGraph, CrewAI, and Model Context Protocol (MCP), including self-improving agents that learn from evaluation feedback. Built for real automation, not chatbot demos. Advanced RAG, Evaluation & Observability 10+ production RAG systems (self-RAG, adaptive retrieval), one serving hundreds of users across thousands of documents. Migrated Pinecone to Weaviate for better recall at lower cost. Every system ships with LLM-as-judge, retrieval metrics, and full tracing (LangSmith/Langfuse), so quality is measured, not guessed. LLM Fine-Tuning & Cost Optimization PEFT (LoRA/QLoRA), SFT, DPO, and instruction tuning. Fine-tuned a 7B Arabic model served on autoscaling serverless GPU, plus multimodal vision-language models. Cut client AI costs by up to 40% through open-source replacement and quantization, with no drop in performance. Multimodal & Document AI OCR and document-extraction pipelines across PDF, DOCX, PPTX, Excel, and images, with strong F1 on messy financial and clinical documents. Also built a temporal, multi-hop knowledge graph over an encrypted Postgres + Qdrant store with client-side encryption. AI Automation & Integrations Connecting LLMs to real business systems: n8n, Make (Integromat), Zapier, CRM automation (HubSpot, GoHighLevel, Airtable), Supabase backends, and Twilio/WhatsApp. AI that plugs into how your team actually works. Enterprise Backend & Scalable Infra Master-level Python (FastAPI, Flask), robust CI/CD, and multi-cloud deployment (AWS, Azure, GCP). Docker + Kubernetes with KEDA autoscaling, plus privacy-first, multi-tenant systems (E2EE, RBAC, audit logging), including HIPAA-conscious PHI handling. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ TECH STACK ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ Agents & LLMs: LangChain, LlamaIndex, LangGraph, CrewAI, MCP, Hugging Face (PEFT/TRL), Ollama, TGI, vLLM ▸ Eval & Tracing: LangSmith, Langfuse, LLM-as-judge, custom eval frameworks ▸ Vector DBs: Weaviate, Pinecone, Qdrant, FAISS, ChromaDB ▸ Models: OpenAI, Claude, Gemini, fine-tuned open-source ▸ Automation: n8n, Make (Integromat), Zapier, Supabase, Twilio ▸ Backend: Python (FastAPI, Flask), TypeScript/Node (NestJS, NextJS), PostgreSQL, MongoDB ▸ MLOps & Cloud: Docker, Kubernetes, KEDA, CI/CD, Airflow, MLflow; AWS (SageMaker, Lambda), Azure ML / Azure OpenAI, GCP, serverless GPU ▸ CV & Data: OCR optimization, vision-language models, Stable Diffusion, web scraping ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHY CLIENTS PICK ME ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ▸ Ships to production. I build AND deploy. You get systems that run 24/7 and scale, not a prototype someone else has to finish. ▸ Proven track record. Top-Rated Plus, 100% Job Success, enterprise and healthcare AI delivered end-to-end. ▸ Innovation-driven. I bring the latest (MCP, adaptive RAG, new model releases) into production. ▸ Cost-conscious. High-performance AI that optimizes spend without compromising quality. ▸ Quality-first. Production-grade code, proper testing, and evaluation built in. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Building an AI product, or need one taken from prototype to production and scaled reliably? Send me the brief and I'll tell you exactly how I'd approach it.

I build ML and AI systems that ship — and I design the evaluation and experiments to prove they work in production. My work spans recommendation systems, forecasting, churn and ETA prediction, and more recently LLM applications including RAG and AI agents. 11 years of production ML at Meituan, Didi, and LINEMAN (Thailand's largest food-delivery platform). Recent results: • Delivery-time (ETA) model — XGBoost with quantile loss, cut long-tail MAE by 6.8% (A/B tested) • Multi-objective ranking (MMoE) + DPP re-ranking for a B2B marketplace — +3.87% GMV per user, +6.53% CTR • RAG question-answering system over 10,000+ company financial reports — embeddings + FAISS + LLM What I can build for you: • LLM apps & RAG: document Q&A and chatbots grounded in your own data (OpenAI / Anthropic / open-source models, FastAPI backends, vector search), with an eval set so you know it works • AI agents & workflow automation: tool-calling agents, structured output, guardrails • Forecasting & predictive modeling: demand, ETA, churn — explainable and A/B-tested • Recommendation systems: retrieval + ranking + re-ranking, cold start, latency budgets Stack: Python, SQL, PyTorch, Spark, FastAPI, LangChain, AWS SageMaker. Based in UTC+8 — I overlap US mornings and EU afternoons.

Need AI Transformation for your business or enterprise ? Have a pile of documents, or a process eating your team's week, that you know AI should handle, but every attempt so far has been a demo nobody trusts? I am your AI guy ! Hi, I'm Bharadwaj. Over the past years I've taken enterprise AI from idea to production for a Fortune 500 manufacturer, a healthcare product that has handled 17,000 calls, and a SOC 2 Type II fintech. I'm certified in RAG quality by Maven, and I hold Top Rated Plus with 100% Job Success. Anyone can build an AI system now, so can I. But AI projects don't die on the software/code layer. They die in the gaps between your vision and your engineer's lack of understanding: the silent week, the scope that quietly drifts, the "almost done" that never ships, the dev who goes dark the moment it gets hard. This is how I'm different. I run your build like a founder who owns the outcome. Replies in hours. A working slice you can test every week. Honest answers, including "that is a bad idea, and here is why," before you spend a dollar on it. You always know where your build stands and what comes next. The hard engineering still happens underneath: every answer cites its source, evaluations run from day one, and I ship under HIPAA or SOC 2 with a signed BAA when your data is regulated. But on a real build, clear communication is what actually ships it. What clients say: "He is one gun AI ML developer. Our new permanent for all AI ML and Python work in future." - Upwork client (5.0) Last updated: 1st August 2026 → Results: - Certified in RAG quality (Maven, Systematically Improving RAG Applications) - Shipped enterprise AI into production under HIPAA and SOC 2 Type II - Cited, multilingual RAG for a Fortune 500 manufacturer - A HIPAA voice system that has handled 17,000 calls - A back-office automation that cut a 4-person, roughly $200,000-a-year task by 80 to 90 percent - 4,406 hours delivered, 5.0 across 19 reviews, 1,600-hour contracts held with 100% client satisfaction. → How I help you: - Validate your AI architecture before you spend a dollar on the wrong stack - Build cited RAG your team actually trusts, over your own documents - Engineer out hallucinations with real evaluation, not vibes - Handle PHI and regulated data under HIPAA or SOC 2, and sign a BAA - Let non-technical staff query your database in plain English with text-to-SQL - Automate the manual, repetitive work quietly eating your team's hours Here is everything I have experience in: → RAG and Retrieval Enterprise RAG, Retrieval Augmented Generation, GraphRAG, multimodal RAG, chunking and embedding strategy, reranking, hybrid search, source citation, hallucination control, permission-aware retrieval, LlamaIndex, LangChain, PropertyGraphIndex, Neo4j → AI Agents and Orchestration LangGraph, CrewAI, AutoGen, MCP servers, multi-agent systems, agentic workflows, tool calling, long-term memory, workflow orchestration. → Voice AI LiveKit, Twilio, Whisper, Eleven Labs, WebRTC, inbound receptionist, outbound calling, telephony, real-time transcription, structured extraction from audio, call analytics → LLMs and Fine-Tuning GPT, Claude, Llama, Gemma, DeepSeek, Mistral, prompt engineering, structured outputs, function calling, reinforcement fine-tuning, LLM fine-tuning, Hugging Face → Vector Databases Pinecone, Qdrant, Chroma, Weaviate, pgvector, Azure AI Search → Evaluation and Observability RAGAS, LangSmith, Langfuse, golden-set evaluation, regression testing, LLM evaluation, prompt testing → Document AI and Extraction PDF, Word, Excel, and PowerPoint ingestion, OCR, table detection, structured extraction, text-to-SQL, invoice and report processing, deduplication → Microsoft and Enterprise Stack Azure, Azure OpenAI, MS Graph, Teams, Outlook, OneDrive, SharePoint, Office 365, single sign-on, Active Directory → Compliance and Security HIPAA, SOC 2 Type II, BAA, RBAC, PHI handling, audit logging, prompt-injection defense, data governance, GDPR-aligned architecture → Backend and Infrastructure Python, FastAPI, Django, Flask, Node.js, Docker, Kubernetes, AWS, Azure, GCP, CI/CD → Data PostgreSQL, MongoDB, Redis, Elasticsearch, data pipelines, ETL → Integrations Salesforce, HubSpot, QuickBooks, Plaid, Stripe, Slack, REST, GraphQL, webhooks → Industries I've built for: Manufacturing, Healthcare, Fintech, Insurance, Logistics and Shipping, Corporate Housing, Legal, Retail and Luxury, SaaS If you're serious about getting AI into production, let's talk. One call can save you a six-month science project and tens of thousands on the wrong build. Tell me the documents your team keeps re-reading or the task eating their week, and I'll send back a short Loom within 24 hours with the architecture and what it saves. Bharadwaj S. | Enterprise AI Engineer