Hanzala Hafeez

Full Stack AI Engineer @Turing | Technical Recruiter@Turing | Agentic AI | Voice AI (STT/TTS) | Cloud Architecture | Python | FastAPI | AWS (EC2, ECS, lambda) | Azure | Azure AI Foundry

I am an AI Systems & Backend Engineer who bridges the gap between frontier AI capability and robust, production-grade software engineering. My focus is taking complex AI products, from Agentic AI networks and real-time Voice AI (STT/TTS) to high-concurrency cloud backends, all the way from initial proof-of-concept (POC) to full production deployment. My technical footprint covers building asynchronous microservices with FastAPI, architecting self-improving LLM agent loops, scaling data pipelines on AWS & Azure, and managing fine-tuning (SFT/RLHF) for state-of-the-art models. Core Engineering Capabilities: • Production Backend & Cloud Architecture: Architecting high-concurrency microservices, event-driven message queues (AWS SQS/S3), Nginx reverse proxies, Docker containerization, and production deployments on AWS and Azure (App Service, AI Foundry). • Agentic AI & Voice Systems: Designing autonomous multi-agent workflows, self-training LLM execution engines, and low-latency Voice AI architectures featuring real-time Speech-to-Text (STT) and Text-to-Speech (TTS) pipelines. • LLMOps, RAG & Model Alignment: Building production-grade RAG architectures with vector search, parameter-efficient fine-tuning (LoRA/Q-LoRA), and leading SFT/RLHF dataset alignment for frontier models. Tech Stack: • Languages & Backend: Python, FastAPI, Flask, RESTful APIs, WebSockets, C++, JavaScript • AI / Voice / Agents: Agentic AI Frameworks, Voice AI (STT/TTS), SFT & RLHF Alignment, Autonomous Agents, RAG Architectures, Vector DBs, PyTorch, YOLO • Cloud & Infrastructure: AWS (Lambda, SQS, S3, EC2, API Gateway), Azure (AI Foundry, App Service), Docker, Nginx, CI/CD • Databases: PostgreSQL, Redis, MongoDB, MySQL