Syed Mohsin S.

🏆 100% JSS | Top Rated ⏱️ Available across US, Canada, Europe, UK, Australia, and preferred time zones 🥇 Experience with teams at Turing, Meta, Google, YouTube (Agentic) 💼 8+ years | 27+ delivered projects | 905+ billed hours Senior AI/ML Engineer LLMs, RAG, Multi-Agent Systems, Python Backends Expert| Deep Learning | Computer Vision I’m Syed Mohsin Ali Shah, Ph.D. (Computer Science), University of Deusto, Spain. I design and deploy production AI that combines Large Language Models, retrieval-augmented generation (RAG), and agentic orchestration with scalable Python services. Focus areas: measurable accuracy, latency reduction, reliability, and maintainability. Core Strengths .) AI & LLM Engineering: GPT-4/5 class, Claude, LLaMA, Mistral; HF Transformers; custom inference servers .) Agentic AI: LangGraph, LangChain, MCP; planner–executor tool use, guardrails, monitoring .) Agentic AI: LangGraph, LangChain, MCP; planner–executor tool use, safeguards, and monitoring .) RAG: Pinecone, Weaviate, Chroma, LlamaIndex; hybrid search, reranking, retrieval evaluation .) NLP: Summarization, NER, structured extraction, document QA, routing Backend & Platforms: .) APIs: FastAPI, Django/DRF, Flask; JWT/OAuth, rate limiting, observability .)Scale & Ops: Docker, CI/CD (GitHub Actions), Redis, Celery, async I/O, streaming .) MLOps: MLflow, experiment tracking, canary releases, automated eval harnesses Machine Learning, Computer Vision & Generative AI: .) Fine-Tuning: LoRA/QLoRA, SFT, preference optimization; prompt/system design .) Vision: YOLO pipelines, OCR (EasyOCR, Tesseract, PaddleOCR), document AI .) Image Gen: SDXL/Flux, DreamBooth, control adapters, safety layers Cloud Based Deployment: .) Cloud: AWS, GCP, Azure, RunPod; GPU hosting, autoscaling, cost control .) Pipelines: ETL/ELT, Spark, batch/stream retrieval layers, vector ETL Quant & Time-Series (Stocks/Crypto): .) Forecasting Models: LSTM/GRU, TCN, Temporal Fusion Transformer, N-BEATS, XGBoost/LightGBM hybrids .) Signal Engineering: multi-horizon forecasts, regime detection, volatility features, market microstructure signals .) Back testing: walk-forward validation, cross-asset/rolling windows, slippage/fees modeling, position sizing .)Deployment: real-time inference APIs, feature stores, event-driven triggers, risk constraints and monitoring .) Use Cases: stock and crypto price prediction, alpha signal research, anomaly detection, and alerting (Research and engineering services only; not financial advice.) What I Deliver: .) Agentic AI platforms (planner/orchestrator + tools: web search, CRMs, booking, calendars) .) RAG chatbots over PDFs, Notion, websites, and email—evaluated for grounding and faithfulness .) High-performance Python backends with FastAPI + Redis for low-latency inference .) LLM cost/latency optimization with caching, prompt shaping, and model routing .) Quant research stacks for equities/crypto: forecasting, signal evaluation, and live endpoints Tooling & Stack: .) Python, FastAPI, Django/DRF, LangGraph, LangChain, MCP, LlamaIndex, HF Transformers, Pinecone, Weaviate, Chroma, MLflow, Redis, Celery, Docker, GitHub Actions, SDXL/Flux, YOLO, OCR toolkits. .) Time-series: PyTorch/Lightning, darts, sktime, prophet (baselines), vector DBs for event retrieval. 💬 Let’s Work Together I build intelligent, reliable, and scalable AI systems from custom GPT apps and multi-agent workflows to high-performance Python APIs with clear communication, fast delivery, and long-term maintainability.