Hire the Best Artificial Intelligence Engineers

Principal AI Engineer and Software Architect specializing in production AI systems: agents, RAG platforms, workflow automation, document intelligence, and full-stack AI applications. I help companies turn complex AI ideas into reliable software products — with the architecture, backend services, APIs, cloud infrastructure, evaluation, observability, security, and deployment practices needed for real business use. My background combines 20+ years of enterprise software engineering with hands-on applied AI delivery across regulated, high-volume environments. I can own both the AI layer and the production system around it. What I build: ▸ AI Agents & Agentic Workflows Custom agents that use tools, call APIs, retrieve data, produce structured outputs, handle multi-step tasks, recover from failures, and integrate with real business workflows. Stack: LangGraph, LangChain, OpenAI, Anthropic Claude, AWS Bedrock, MCP, tool/function calling, memory, state machines, human review, tracing, evaluation, and guardrails. ▸ Production RAG & Knowledge Systems RAG systems for documents, logs, runbooks, tickets, policies, contracts, and internal knowledge. I design ingestion, chunking, embeddings, hybrid search, BM25 + vector retrieval, reranking, citations, grounding, evaluation, and hallucination reduction using pgvector, PostgreSQL, OpenSearch, Pinecone, Weaviate, FastAPI, Claude, OpenAI, and AWS Bedrock. ▸ AI Automation Systems LLM-powered automation for finance, claims, support, operations, research, reporting, document workflows, approvals, and legacy processes. I connect AI to APIs, databases, queues, SaaS tools, internal systems, and human-in-the-loop review. ▸ Full-Stack AI Applications AI assistants, internal copilots, document extraction tools, AI search products, workflow apps, dashboards, SaaS platforms, and customer-facing AI products. ▸ Software Architecture & Backend Engineering Java, Kotlin, Spring Boot, Python, FastAPI, Node.js, TypeScript, React, Next.js, PostgreSQL, MongoDB, Redis, Kafka, REST, GraphQL, microservices, event-driven architecture, system design, and API integrations. ▸ Cloud, DevOps & Production Readiness AWS, AWS Bedrock, Docker, Kubernetes, Terraform, CI/CD, monitoring, logging, cost-aware deployment, security review, observability, and production operations. Selected production experience: • Built production RAG with hybrid BM25 + vector retrieval, reciprocal rank fusion, reranking, citation-grounded streaming, and a CI-gated evaluation harness that improved faithfulness from 0.81 to 0.95. • Built LangGraph agents with explicit state machines, conditional replanning, validated tools, Postgres checkpointing, LangSmith tracing, and resumable execution after process failure. • Built MCP servers exposing typed tools for sandboxed SQL, HTTP/GraphQL, and semantic search across stdio, HTTP, and SSE. • Led AI-driven automation for high-volume claims workflows, including ingestion, validation, transformation, exception handling, and LLM-assisted legacy rule extraction. • Built agent-based transaction analysis using REST, Splunk, and AI agents for real-time financial data; finalist in a Finance Technology AI Hackathon. • Architected enterprise backend and data platforms using Java/Spring Boot, Kafka, PostgreSQL, Oracle, AWS/Azure, Terraform, Docker, Kubernetes, CI/CD, logging, tracing, and production runbooks. • Led delivery across financial services, healthcare, retail, hospitality, publishing, and technology clients, including distributed teams up to 55 engineers and multi-vendor integrations. Why clients hire me: • I can own the full system: AI workflow, backend, frontend, database, cloud, and deployment. • I design for reliability, evaluation, observability, security, cost control, and maintainability. • I understand regulated environments, PII, audit trails, compliance constraints, and production risk. • I can operate as hands-on builder, architect, technical lead, and stakeholder-facing delivery partner. Core stack: AI Agents, RAG, LLM Apps, LangGraph, LangChain, MCP, OpenAI, Anthropic Claude, AWS Bedrock, Tool Calling, Function Calling, LLM Evaluation, RAGAS, LangSmith, pgvector, Pinecone, Weaviate, OpenSearch, Python, FastAPI, Java, Kotlin, Spring Boot, Node.js, TypeScript, React, Next.js, PostgreSQL, MongoDB, Redis, Kafka, AWS, Docker, Kubernetes, Terraform, CI/CD. Send me a short description of what you want to build, what systems/data it needs to connect to, and what outcome matters. I’ll help define the architecture, risks, milestones, and practical path to production.

AI Architect & Senior ML Engineer with 10+ years of academic + industry experience building production-grade intelligence systems, LLM pipelines, and multimodal AI. I design and deploy end-to-end ML architectures, including LLM orchestration, timeseries modeling, graph ML, OCR pipelines, and automated image-processing systems. Trusted by enterprises for high-reliability AI, data privacy, and scalable ML infrastructure.

⭐𝗘𝗫𝗣𝗘𝗥𝗧-𝗩𝗘𝗧𝗧𝗘𝗗 𝗧𝗢𝗣-𝟭% 𝗢𝗡 𝗨𝗣𝗪𝗢𝗥𝗞⭐ 🚀 𝗧𝗼𝗽 𝗥𝗮𝘁𝗲𝗱 𝗣𝗹𝘂𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗳𝗿𝗼𝗺 𝟏𝟐 𝗬𝗲𝗮𝗿𝘀🔥 𝟭𝟬𝟬% 𝗝𝗼𝗯 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 I’m a senior AI/ML Engineer & Generative AI Specialist with over a decade of experience building real-world intelligent systems. I develop AI products that make a measurable impact—whether that’s automating health policy premiums through facial analytics, optimizing logistics via predictive modeling, or powering chatbots with cutting-edge LLMs. 🧠 Core Expertise Generative & Language Models: GPT‑4o, GPT‑4 Mini, GPT‑3, BERT, LLaMA, Mistral; custom fine‑tuning, retrieval‑augmented generation (RAG), prompt engineering. Computer Vision & Audio: YOLO, Faster R‑CNN, UNet, DeepLab, OCR; image quality analysis, facial attribute detection (BMI/smoker/age), sound classification, speech‑to‑text. Machine Learning & MLOps: XGBoost, LightGBM, CNNs, RNNs, transformers; TensorFlow, PyTorch, Keras, LangChain, Hugging Face; deployment via Docker, Kubernetes, CI/CD pipelines. Deployment & Infrastructure: AWS, GCP, Azure, Databricks, Vertex AI, Sagemaker; FastAPI/Flask microservices; vector databases (Weaviate, Pinecone); ETL & orchestration with Airflow and PySpark. APIs & Integrations: REST, GraphQL, OAuth/JWT, WebSockets; Twilio, Slack, Discord, WhatsApp Business, Google Cloud APIs, Stripe. 🎯 Impact & Achievements Built facial analytics models to estimate age, BMI, and smoking status, enabling automated insurance pricing and risk assessment for thousands of policies. Designed multimodal LLM workflows using LangChain and LlamaIndex, delivering context-aware chatbots and knowledge retrieval systems. Deployed scalable inference pipelines on AWS and Kubernetes, ensuring high availability and cost‑effective resource use. 🤝 Why Work With Me Expert‑Vetted & Top‑Rated Plus: Ranked in the top 1% on Upwork with 100% job success. Business Value First: I translate AI research into practical, cost‑saving solutions. Clear Communication: I avoid jargon and keep stakeholders informed at every step. End‑to‑End Ownership: From ideation to deployment and maintenance, I deliver comprehensive solutions. 📩 Let’s build AI that truly delivers—drop me a message to discuss your project!

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.

I am an AI engineer with 4 years of experience building and deploying production ready AI systems across computer vision, NLP, and generative AI. I have worked on everything clients need right now: fine tuning and training deep learning models, building RAG and GraphRAG pipelines, LLM powered applications, OCR and document extraction, real time face recognition and multi object tracking, classification systems, embedding pipelines, and end to end data workflows from raw input to deployed output. I also have experience with model optimization including distillation, pruning, and quantization for edge and cloud deployment. On the engineering side I am comfortable with FastAPI, PostgreSQL, pgvector, Python async, and cloud and GPU based deployments. I have built and shipped full stack AI products, not just models. I also lead a team of AI engineers, so I understand both deep technical execution and what it takes to deliver consistently on real projects. If you have an AI problem that needs to actually work in production, I can build it.

Need to further optimize your LLM/GPT/RAG application performance with the support of a top LLM expert, or start from scratch? 6+ Years, AWS and Microsoft Azure Certified AI/ML Specialist, I am focused on building Advanced AI applications around Large Language models (GPT, Claude, Llama, Gemini, Azure OpenAI, etc.), and have compiled some of the most cutting-edge features in AI, RAG, and NLP 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 significantly 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, ✦ Model Deployment ✦ Model Fine-tuning ✦ Knowledge Extraction ✦ Structured Data Query (Connect your database with LLMs) ✦ AI Voice/Speech Agents ✦ MCP & A2A (Connect your APIs and AI Agents) ✦ GraphRAG ✦ RAG Enhancement ✦ Prompt Engineering ✦ Long-term Memory and Continual Learning (For an LLM/AI, context is gold) ✦ LLM Performance Analysis (Evaluation, Governance, and Monitoring) 🔧 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, LlamaIndex, Haystack, AdalFlow, GraphRAG, LangGraph - 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.com, 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. Book my consultation if you're new to the concept and want to save several hours searching for the right direction.