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Vetted FAISS Professionals
Pre-screened and vetted.
Mid-level ML & Full-Stack Engineer specializing in LLM systems and RAG
Mid-level Full-Stack Engineer specializing in healthcare, mobile apps, and AI
Mid-level Data Scientist / AI Research Engineer specializing in LLMs, RAG, and applied ML
Intern Data Scientist specializing in LLM agents, RAG, and real-time ML pipelines
Mid-level Software Engineer specializing in cloud microservices and AI search
Mid-level AI/ML Engineer specializing in LLM-powered RAG systems and MLOps
Mid-Level Full-Stack Software Engineer specializing in LLM-integrated web applications
Mid-level Full-Stack/AI Engineer specializing in LLM microservices, RAG, and data pipelines
Mid-Level Full-Stack Software Engineer specializing in healthcare web apps and LLM integrations
Mid-level AI/ML Engineer specializing in LLM agents, RAG pipelines, and AI automation
Mid-level Full-Stack Developer specializing in Java/Spring Boot, React, and AI/ML APIs
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP/RAG
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Mid-level Full-Stack Developer specializing in React, Node.js, and AI automation
Junior Generative AI Engineer specializing in LLM systems and RAG
Entry-Level Software Engineer specializing in healthcare data and AI-enabled tools
Senior Data Scientist / ML Engineer specializing in NLP, speech AI, and computer vision
Mid-level Software Engineer specializing in Python automation and GenAI on AWS
Junior Machine Learning Engineer specializing in Generative AI and LLM agents
Mid-Level Full-Stack Software Engineer specializing in web apps and AI-powered tools
Junior AI Engineer specializing in production RAG systems and GPU-accelerated inference
Junior Software/AI Engineer specializing in LLM agents and RAG systems
Muhammad Midhat
Senior Full-Stack/Backend Engineer specializing in APIs, distributed systems, and AI integrations
“AI/backend engineer who has built and scaled production LLM-powered SaaS features (document assistant + compliance review agent) on a Node.js/TypeScript + Postgres/Redis stack deployed to GCP Kubernetes. Demonstrates strong production reliability chops—async queueing, autoscaling, observability, and database tuning—with quantified wins (p95 latency -60%, query 4s to <200ms) and robust AI guardrails (strict RAG, schema validation, citations, HITL).”