Ahmed J.

Senior AI/ML Engineer | GenAI, RAG & LLM Fine-Tuning | FastAPI, Django

✅ $300K+ Earned | Top Rated Plus | 8,000+ Hours | 38 Jobs | 100% Job Success I build production-grade LLM, RAG, and AI Agent systems. Not prototypes. Not API wrappers. Real Python pipelines, deployed on AWS, handling real traffic. If your AI needs to actually work in production, let's talk. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🤖 LLM AND GENERATIVE AI ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ End-to-end LLM applications using OpenAI (GPT-4o), Claude, Gemini, LLaMA, and Mistral. Full RAG pipelines over your documents, databases, and knowledge bases. AI Agents and multi-agent workflows built on LangChain, LangGraph, and LlamaIndex. LLM fine-tuning with LoRA, QLoRA, PEFT, RLHF, and DPO on open-source models. Prompt engineering, tool calling, memory patterns, guardrails, tracing, and LLM evaluation harnesses. If your team is moving an LLM prototype to production, this is exactly where I work. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚡ FASTAPI AND DJANGO BACKENDS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ FastAPI and Django are my backend stack of choice for AI-powered services. → Async REST APIs: FastAPI + Pydantic + SQLAlchemy + PostgreSQL → Scalable Django backends: DRF, Celery, Redis, multi-tenant SaaS architecture → FastAPI as the serving layer for LangChain, RAG, and agent endpoints → Auth systems: JWT, OAuth2, RBAC → Microservices, Docker, Kubernetes, CI/CD via GitHub Actions and GitLab CI → Swagger / OpenAPI documentation, clean architecture, full test coverage ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎙️ VOICE, AUDIO, VIDEO AND TEXT AI ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ → Voice AI agents and real-time speech pipelines (Whisper, AssemblyAI, Deepgram, ElevenLabs, VAPI, Retell AI) → STT / TTS integration, NLP, NER, and multimodal LLM pipelines → Computer Vision: YOLO, OpenCV, CNNs, Vision Transformers → Diffusion pipelines and image / video generation workflows → Audio transcription, classification, and processing at scale ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🧠 ML, MLOPS AND VECTOR SEARCH ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ML Stack: PyTorch, TensorFlow, HuggingFace, scikit-learn, XGBoost Vector DBs: Pinecone, Weaviate, Milvus, FAISS, pgvector, Elasticsearch Serving: vLLM, TGI-style inference, batching, caching, routing, streaming responses MLOps: MLflow, DVC, Prefect, Ray, eval harnesses, regression testing, tracing Cloud: AWS (SageMaker, EKS, EC2, Lambda, Bedrock, DynamoDB, CloudFront), Terraform, GPU providers ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏆 WHAT MAKES ME DIFFERENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Most AI freelancers deliver a Jupyter notebook or a thin wrapper around an API. I architect and ship the full system. I have 14+ years of engineering experience and a background as a Fractional CTO and AI/ML Team Lead. I have led teams and personally written the code. I think in systems, delivery, and cost control, not just individual features. At Alethea AI, I was part of the team that built a cloud-native architecture on AWS capable of accommodating 30 million visitors, highlighted in an official AWS case study. The platform publicly closed a $16M token sale. That is the level of production reliability I bring to every engagement. ✔ Architecture designed for scale from day one ✔ Clean code, full documentation, and production observability ✔ I write the code myself, no offshoring your project ✔ Reliable delivery: timelines, milestones, and accountability ✔ Cost-controlled cloud and GPU workloads ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🤝 WHO I WORK WITH ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Startups validating AI-first MVPs fast. Scaleups moving LLM prototypes to production. Product teams that need a senior AI/ML engineer who can own the full stack, from fine-tuned model to FastAPI endpoint to deployed AWS service. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📩 Message me with your current state, timeline, and constraints. If I can help, I will tell you exactly how, usually within a few hours.