Hire the Best Artificial Intelligence Engineers

I build production-ready RAG chatbots, voice AI agents, and autonomous AI systems — designed and deployed to hold up under real usage, not just demos. My work covers the full pipeline: retrieval architecture, agent design, voice integration, and cost optimization, so the LLM bill doesn't become a second project six months in. I don't just wire up an API to a model, I build systems that keep answering correctly and keep costing what they should, long after launch. My expertise includes: RAG Chatbots & Retrieval Architecture (Hybrid Search, Re-ranking, Semantic Caching) Autonomous AI Systems & Multi-Step Agent Workflows (LangGraph, Tool Calling, Memory) Voice AI Agents & Conversational IVR (Inbound Calls, Appointment Booking, Automated Follow-ups) LLM Cost Optimization (Query Compression, Token Budgeting, Sentence-Aware Chunking) Vector Databases & Semantic Search (Pinecone, pgvector, ChromaDB, FAISS) AI Workflow Automation & Custom Chatbots / Internal Knowledge Bases Full-Stack AI Applications (Python, FastAPI, n8n, Zapier) Cloud Deployment & Integration (AWS, Google Sheets/Calendar API, Docker) Recent projects include: Legal Document Intelligence Platform — Designed a RAG engine for plain-language Q&A over complex legal documents, plus a generation engine that drafts custody agreements, contracts, and divorce filings from structured input. Cut manual drafting time from hours to minutes. Shopify Live RAG Chatbot Pipeline — Built a crawler that continuously feeds live product data from a client's Shopify store into a RAG chatbot, giving customers accurate, real-time answers with zero manual updates. Voice AI Agent for a Salon (Vapi) — Built an inbound voice assistant that answers calls, books appointments directly into Google Sheets, and sends automated confirmation emails — full hands-off front desk automation. LeanRAG — Cost-Optimized RAG Architecture — Implemented four layered cost-optimization techniques (semantic caching, query compression, token budget enforcement, sentence-aware chunking) that cut LLM inference costs by 30–40% without losing answer quality. Karachi Air Quality Index Forecasting System — Built an end-to-end ML pipeline (data collection, preprocessing, model training, deployment) as a usable predictive tool for a non-technical team. What you can expect: Clean, maintainable, production-quality code with clear documentation Retrieval and voice systems tuned for accuracy, not just plausible-sounding answers Cost-aware architecture — token budgets and optimization built in from day one Clear communication and reliable post-deployment support I work with startups, growing businesses, and teams building AI products for measurable results, not experimental prototypes. If you're dealing with a support bottleneck, need a RAG chatbot that actually knows your documents, an autonomous system that runs a workflow end-to-end, or an LLM bill that's gotten out of hand, message me and I'll recommend the most practical approach.

AI Engineering Lead and PhD researcher in AI/ML, certified in Claude by Anthropic. I design and ship production systems built around Claude: agents, RAG pipelines, and automations that actually make it to deployment Over the past 7 years I've helped more than 20 companies put AI into production across the US, Europe, and the Middle East. I've led engineering teams at startups large and small, and I bring a consistent track record as a high performer on the work I take on I post regularly on Medium, X, and YouTube on the latest in AI, ML, and tech, which keeps me on top of how fast the field moves, with an audience of over 10,000 across platforms. I share this work publicly partly because teaching a thing is the best test of whether you understand it What I build: - RAG chatbots and agents over your documents, PDFs, Notion, and knowledge bases - LLM fine-tuning on your domain data - Workflow automations that replace 40–80% of manual operations - Solution architecture, so you commit to the right stack the first time - Recovery work on stalled or underperforming AI projects Send over the project and I'll reply the same day with a plan, a clarifying question, or an honest pass

Most Machine Vision projects fail between the prototype and production. I've shipped 54+ that didn't. ⚙️YOLO Detection | Pose Estimation | Object Tracking | AI Agents | LLM Integration Sports & Fitness AI | CCTV & Surveillance AI | Retail AI | Healthcare AI You have a working concept... or a clear problem involving cameras, video, or image data. The challenge is making it fast, accurate, and stable under real-world conditions. Wrong framework choices. Inference too slow for live video. Models that break the moment lighting, angle, or environment changes. And systems that detect things but can't reason about them or act on them autonomously. That's exactly where most builds stall. I design and build real-time computer vision pipelines that go all the way... from model training to live deployment... and increasingly, from visual perception to autonomous AI agents that understand, decide, and narrate. LLM APIs (OpenAI, GPT-4o, Gemini, Claude) | AWS (EC2, S3, Lambda) | Azure Cloud Services | MLOps & API Integration | Model Deployment & Scaling While most CV engineers stop at training the model, I go further: → High-speed inference optimization using TensorRT, ONNX, OpenVINO, FP16/INT8 (up to 5× faster) → LLM agents integrated with vision pipelines for alerts, reasoning, and automation → Mobile AI deployment using Core ML (iOS) and TFLite (Android) with 10+ shipped apps → Edge AI deployment on Jetson, OpenVINO, CUDA, and embedded systems → End-to-end pipelines: data → training → optimization → real-time deployment Key Accomplishments: ⭐ $5M+ revenue from AI solutions ⭐ 100+ computer vision systems delivered ⭐ Built and launched 2 SaaS products ⭐ Real-time sports AI (7+ sports, 15+ teams) ⭐ 10+ mobile AI apps (iOS Core ML, Android TFLite) ⭐ Production AI for surveillance, industrial & safety use cases ⭐ Medical imaging AI deployed in 5+ hospitals ⭐ Up to 5× faster inference (ONNX, TensorRT, FP16/INT8) ⭐ Large-scale tracking & re-ID (1M+ labeled data) ⭐ Agentic AI systems for autonomous decision-making If you have read this far, please note that I appreciate you taking the time to learn about me. Personally, it’s been an amazing journey and knowledge exercise to get to this level of competence in AI and software development. Domain Expertise: ✅ athlete tracking | shot detection | scoring | drill analysis | pose estimation ✅ defect inspection | PPE compliance | staff monitoring | meter reading | quality control ✅ ANPR | crowd monitoring | people counting | intrusion detection | perimeter security ✅ tumor detection | ultrasound | X-ray/CT analysis | lesion segmentation | medical imaging ✅ aerial monitoring | traffic flow | license plate recognition | vehicle & accident detection ✅ customer analytics | receipt extraction | shelf monitoring | inventory tracking Tech Stack: YOLOv5–YOLOv8–YOLOv11, Detectron2, MMDetection, DeepSORT, StrongSORT, MediaPipe, OpenPose, Pose Estimation, Action Recognition, Segmentation (semantic & instance), OCR, anomaly detection, object tracking, PyTorch, TensorFlow, TFLite, Core ML, OpenCV, FastAPI, Flask, ONNX, TensorRT, OpenVINO, CUDA, AWS, Azure, GCP, edge AI, mobile AI, real-time inference, video analytics, AI automation, LLM integration (GPT-4o, Claude, Gemini, Groq), LangChain, LangGraph, CrewAI, RAG systems. 💬 If your project involves cameras, video, or images... and you need it fast, accurate, fully deployed, and intelligent enough to reason and act autonomously... I am the engineer you are looking for.

EX Astrophysics Researcher Now helping companies build AI systems that stay relevant long after today’s AI models become obsolete. Six months ago, everyone wanted RAG. Then Agentic RAG. Then Knowledge Graphs. Now it's Context Engineering and a new "breakthrough" every other week. Here's the uncomfortable truth: your hardest problem isn't choosing the right AI model. It's that by the time you've hired engineers to build with it, the ground has already shifted. And even if you hire brilliant engineers… Who designs the architecture? Who decides whether you need RAG, fine-tuning, or honestly just a better-engineered context? Who makes sure what you build isn't technical debt six months from now? Who connects product, data, backend, cloud, and business strategy into one system that actually ships? Most companies don't need another freelancer. You're responsible for business outcome not for building an AI team. Even if you build a great in-house team, it eats the one thing you can't buy back: your time and focus. That's where we come in. A high-paced AI consulting partner you can rely on a 40-member team covering design, engineering, research, and deployment, built to understand your business fast and own the journey. That’s exactly where we come in: Over 6 years, I’ve led cross-functional teams of designers, product managers, AI researchers, machine learning engineers, backend developers, App developers, IOT engineers, cloud architects, and DevOps specialists to build AI systems that businesses actually depend on. Whether you’re building an AI-native product, modernising an existing platform, or automating internal operations, we become an extension of your team handling everything from product strategy and AI architecture to development, deployment, optimisation, and long-term support. So instead of managing five different vendors or trying to build an expensive in-house AI team… You get one experienced partner responsible for the outcome. Btw Im Vivekjyoti Bhowmik, a 3× AI Founder who spends every day turning cutting-edge AI research into scalable products that can fit the market to solve real business problems. Today I lead three companies: → TOINGG : an AI Communication OS cum AI first CRM that have generated $700M+ in qualified sales pipeline and still counting. → PGAGI : a 40+ member AI engineering studio in Bangalore, where we design, build, and scale production-grade AI systems for startups and enterprises worldwide. (The reason you’re reading this profile.) → BigAIRLab: our AI research lab, where we continuously evaluate the latest breakthroughs and turn promising research into practical, market-ready products. :trophy: A Few Selected Results: → $700M+ qualified sales pipeline generated through AI-powered outreach, lead qualification, and automated engagement systems. → 50,000+ users in month one and $2.5M revenue generated within two months of launch for Branify, we designed and built. → 2M+ financial signals processed daily through AIMI Brain, powering real-time AI-driven market intelligence. → 45% higher email open rates and 3× faster campaign creation with Email Love, an AI platform (40K + users) for autonomous email generation and optimisation. → 45% reduction in product returns and 3× higher customer engagement with Mirror Me, an AI-powered virtual try-on platform. → 80+ production AI systems delivered across healthcare, fintech, SaaS, manufacturing, retail, and enterprise operations. → 70%+ of the AI products we build are live, actively used by real customers, and generating measurable business value. → Production-grade AI communication infrastructure built to support high-volume voice workflows with scalable cloud architecture and enterprise reliability. → Trusted by businesses across the US, UK, GCC, Africa, Australia, and India. We're a fit if: your budget is min $5,000 as we believe in quality over quantity. Honestly we have lot of invites so choose who to work for outcomeMaxing and this is must: you have 5+ years in business and atleast $1M in revenue. How we work (and why founders like it): We don't bill hourly. Hourly billing means you pay for time and hope for results Ours is different: you only pay when you get the outcome. We sit down before anything starts and map your project using our Power Law Roadmap finding the 20% of milestones that drive 80% of your business outcome. We build those first. Simple things that scale, before clever things that don't. Every milestone comes with a defined outcome, in writing. You don't approve the milestone — and don't pay it — until that outcome is in your hands. That's not a promise; that's how our contracts are structured. As a founder myself, I'll also tell you what most vendors won't: half the ideas on your list shouldn't be built yet. We'll say so. Two ways to start: 1. Message me with what you're building one paragraph is enough.

Healthcare AI built by an engineer who knows buyers are right to filter out generalists. Medical imaging on DICOM, clinical NLP that handles negation and hedging, HIPAA-aware PHI pipelines, patient-facing apps that respect privacy for hospitals, radiology centres, pharma R&D, CROs, medical device makers, health-tech teams. I work with the Brainy Neurals team as the healthcare-AI engineer. My focus sits between "we have clinical data" and "validated AI helping clinicians" model design, PHI handling under your BAAs, on-prem vs cloud, validation against radiologist ground truth, shipping into EHR or device workflow. WHAT I BUILD / IN HEALTHCARE Medical imaging AI — DICOM ingestion, de-identification per Safe Harbor, organ and lesion segmentation on CT and MRI, abnormality detection, NIfTI and ITK pipelines, 3D Slicer. Built on MONAI, nnU-Net, TotalSegmentator, RadImageNet, validated against radiologist consensus. Clinical NLP and document AI note summarisation, ICD and SNOMED mapping, negation and hedging, family-vs-patient history disambiguation, medication extraction, lab-report parsing, prescription OCR. The grammar of clinical text is its own thing; I treat it accordingly. Pharma and clinical research clinical protocol to eCRF extraction (demographics, vitals, inclusion-exclusion, study-arm), systematic review using RoB2, GRADE, PRISMA, citation validation, batch record digitisation, evidence assembly. Patient-facing apps GI symptom-trackers with AI food and lifestyle recommendations, diabetic and gut-condition dish-suggestion, telehealth onboarding, chronic-care intake, mental-health support. Flutter or React Native fronts, FastAPI backends. Hospital workflow AI appointment automation, triage, hand-hygiene monitoring, fall-detection in wards (pose-based, no face recognition), bed-occupancy. THE STACK / FOR MEDICAL & PHARMA Imaging: MONAI, DICOM, NIfTI, ITK, 3D Slicer, nnU-Net, TotalSegmentator, RadImageNet, modality fusion. Clinical NLP and docs: Docling for medical PDFs, DocTR and TR-OCR for prescription and lab OCR, GPT-4o multimodal for complex layouts, Claude and Gemini for clinical reasoning, regex and Pydantic validation, NegEx-style negation with LLM verification. Knowledge: Neo4j for clinical knowledge graphs (drug-drug interaction, condition-symptom, contraindications). pgvector or Qdrant for medical literature. RAG over institutional protocols, authoritative sources only. Infrastructure: FHIR and HL7 v2 with EHRs, OMOP CDM for research, FastAPI for clinical APIs, Flutter and React Native for patient apps, AWS and Azure inside client BAAs, on-prem Ollama and vLLM where data cannot leave. WHO I BUILD FOR Hospitals and clinics radiology AI, patient-flow analytics, EHR decision support, ward-safety. Radiology centres, DICOM pipelines, organ and lesion segmentation, second-read AI, reporting integration. Pharma R&D and CROs clinical protocol extraction, systematic review, eCRF generation, evidence assembly. Medical device (pre-clearance) model prototyping, validation harnesses, dataset curation. I do not claim FDA-cleared deliverables; I build the substrate that goes through your regulatory team. Telehealth and health-tech patient intake, AI triage, multilingual symptom-checker. Specialty practices dental, dermatology, gastroenterology, oncology, mental health. Specialty-tuned models on small datasets. Health insurance claim review, prior-auth triage, appeals support, evidence extraction. HOW I WORK / WITH HEALTHCARE BUYERS Discovery is a 30-minute call: data sources, clinical workflow, compliance posture (HIPAA, GDPR, local). By the end I tell you whether the use case is feasible, validation plan, where ground truth comes from, how PHI flows. If it touches a regulated device pathway, I tell you what is in scope for me and what your regulatory team owns. Pricing is fixed-scope per milestone feasibility, dataset and PHI design, model build, validation, integration handoff. Hourly only for maintenance after deploy. THE BRAINY NEURALS BACKING I work with the Brainy Neurals team 15 AI engineers, NVIDIA Inception Partner, AWS Activate, Microsoft for Startups. When a project needs ward-monitoring cameras, edge deployment on hospital hardware, RAG over clinical docs, or workflow automation around the AI, that capacity sits with the team I bring them in cleanly, you do not manage multiple vendors. For pure healthcare model and clinical-NLP work I lead end to end myself. LET'S TALK / IF You are inside healthcare or pharma, you have a clinical workflow AI can genuinely help with, you understand the validation and compliance work this requires, and you want a senior engineer who has built medical imaging, clinical NLP, and patient-facing apps before not a generalist learning HIPAA on your project. Tell me your data, workflow, compliance. I reply within 24 hours with feasibility, validation approach, milestones.

I'm a Principal Engineer with 20+ years building enterprise systems that stay up, now focused on production AI: AI agents, LLM applications, and RAG. I ship agentic workflows, retrieval pipelines, and LLM integrations on rock-solid Java + AWS foundations — engineered with the rigor to survive real load, real users, and real scrutiny, not just impress in a proof-of-concept. ⚡ AI Agents · LLM Apps · RAG · MCP · Java · Spring Boot · Kafka · AWS PLATFORM ENGINEERING & ARCHITECTURE ▸ Backend & distributed systems — Java/Kotlin (Spring Boot) as the core, plus Python (FastAPI). Event-driven microservices, Kafka pipelines, REST and GraphQL APIs, PostgreSQL. Systems designed to scale, fail gracefully, and pass security review. ▸ Cloud & platform engineering — AWS, Docker, Kubernetes, Terraform, CI/CD. Reusable platform patterns adopted across multiple teams. ▸ Technical leadership — 20+ years leading architecture and hands-on delivery; guided teams from 10 to 55+ engineers across multiple time zones. AI, LLM & AUTOMATION ▸ AI agents & agentic workflows — multi-step agents with LangChain and LangGraph. Tool calling, structured outputs, error recovery, and MCP (Model Context Protocol) servers that expose your tools and data to LLMs cleanly. Agents that complete real tasks, not chatbots that loop. ▸ Production RAG — hybrid search (BM25 + semantic), vector databases (pgvector, Pinecone, Weaviate, OpenSearch), grounding guardrails and evaluation so your AI cites real sources, not hallucinations. ▸ LLM integration — AWS Bedrock, Anthropic Claude, OpenAI. Prompt versioning, JSON-schema enforcement, and CI/CD regression evals so prompts do not silently break when you ship. ▸ AI automation — wiring LLMs and agents into existing business systems to remove manual work, with auditable, deterministic results. END-TO-END DELIVERY ▸ The platform and the product around it — my core is backend and platform (Java, Kafka, AWS) plus the AI running on it, and I take it all the way to a working product with React/Next.js when a project needs it. One senior engineer who owns the platform, the AI, and the deployment — no coordinating between a backend dev, an AI engineer, and a DevOps engineer. WHAT YOU GET WORKING WITH ME ▸ Real production delivery — monitoring, evaluation, and guardrails. Not notebooks. Not demos. ▸ Engineering judgment from 20+ years of distributed systems — whether the job is a Java platform, an AWS build-out, or an AI agent, it sits on infrastructure designed to scale and pass security review. ▸ Regulated-environment fluency — PII handling, audit trails, compliance constraints, and security review are familiar territory (financial-services background), not new problems to solve on your project. PROJECTS I DELIVER WELL ▸ Designing and building scalable backends, APIs, and event-driven platforms ▸ AWS architecture, cloud migration, and infrastructure-as-code ▸ Turning an AI prototype or proof-of-concept into a real production system ▸ Building LLM agents, RAG search, and document extraction into existing applications ▸ AI automation of manual business workflows ▸ Building for regulated industries (finance, healthcare, legal) with compliance built in CORE STACK Languages & Backend · Java · Kotlin · Spring Boot · Python · FastAPI · Node.js · TypeScript · REST · GraphQL · Microservices · Apache Kafka · Event-Driven Architecture Cloud & Platform · AWS · AWS Bedrock · Kubernetes · Docker · Terraform · CI/CD AI & LLM · LLMs · RAG · LangChain · LangGraph · MCP · AI Agents · OpenAI · Anthropic Claude · Vector Databases (pgvector · Pinecone · Weaviate · OpenSearch) · Prompt Engineering · LLM Evaluation Frontend · React · Next.js · TypeScript · Tailwind CSS Data · PostgreSQL · MongoDB · Redis · pgvector HOW WE START Send me a short message describing your project. I respond within a few hours during business days. If it looks like a good fit, we book a free 20-minute scoping call where I give you an honest read on feasibility, approach, and timeline — whether you hire me or not.