Ajay J.

I help companies turn AI from a demo into a production system that is fast, secure, affordable and actually used. Principal AI Engineer with 14+ years in AI/ML and Upwork Expert-Vetted (top 1%), 100% Job Success. Right now I am the technical owner of a national-scale GenAI platform: I built and run a 32x NVIDIA B200 GPU cluster serving GLM-5.2 (753B parameters) to 1,024 concurrent users, plus the RAG, speech and computer-vision products on top of it. WHAT I CAN BUILD FOR YOU - AI Agents & Automation: multi-agent systems with LangGraph, CrewAI, OpenAI Agents SDK and MCP. Tool calling, memory, human-in-the-loop, and guardrails so agents are safe to put in front of customers. - RAG & Enterprise Knowledge AI: hybrid search (dense + sparse), rerankers, GraphRAG (Neo4j, NebulaGraph), Qdrant/Pinecone/pgvector, document AI and OCR, with evaluation so you can prove answers are accurate. - LLM Infrastructure & Cost Reduction: self-host open models (Llama, Qwen, GLM, DeepSeek, Mistral) with vLLM, SGLang or TensorRT. Quantization, multi-GPU parallelism, autoscaling. Cut your OpenAI/Anthropic bill and keep your data private. - Fine-tuning: SFT, LoRA/QLoRA and full fine-tunes for your domain, tone or language, including low-resource languages. - AI Security: LLM red-teaming and penetration testing, prompt-injection defense, PII redaction, access control and safe agent sandboxes. I built a multi-agent LLM pentesting engine with 22 specialist agents. - Voice & Vision AI: real-time voice agents, speech-to-text, text-to-speech and voice cloning; object detection, video analytics, face and health analytics. - AI Strategy, Architecture & Delivery: system design, build-vs-buy, cost and timeline estimates, roadmaps, and technical leadership of your team from PoC to production. RECENT RESULTS - 753B-parameter LLM in production on 32x B200 GPUs at 1,024 concurrent requests - Insurance AI portal that automated claims documents and cut underwriter workload by 80% - Uzbek speech-to-text at 10.54% WER, plus custom neural TTS voices - RAG platform with 256 vector collections and A/B-tested chunking strategies - Fire/smoke detection across 7 live cameras (mAP50 0.761) with LLM-based alert review - Sports-prediction engine later adopted by ESPN Sports HOW I WORK 1. We agree on measurable targets first: accuracy, latency, cost per request, uptime. 2. I build in your cloud or on your hardware, in your repo. You own everything. 3. You get clean code, CI/CD, monitoring (Prometheus/Grafana), documentation and a handover, not just a model file. 4. Clear weekly updates and straight answers. If a simpler or cheaper option is better, I will tell you. TECH STACK LLMs: GPT, Claude, Gemini, Llama, Qwen, GLM, DeepSeek, Mistral Agents & RAG: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, MCP, DSPy Serving & Infra: vLLM, SGLang, TensorRT, CUDA, Docker, Kubernetes, Helm, AWS, GCP, Azure ML: PyTorch, TensorFlow, Hugging Face, MLflow Backend & Data: Python, FastAPI, Node.js, PostgreSQL, MongoDB, Redis, Kafka, RabbitMQ, Elasticsearch, Qdrant, Neo4j Industries: insurance, healthcare, finance, government, media. Send me a short message about what you want to build or where your AI project is stuck. I will reply with a clear plan, timeline and estimate within 24 hours.