Pakistan
I’m Mohib Ahmed, a Machine Learning Engineer with a Bachelor’s in Artificial Intelligence, and a passion for turning data into impact. Having 1+ year of hands-on experience in building and deploying AI solutions from classic models like decision trees and logistic regression to advanced agentic AI and LLM-driven pipelines, I know how to pick the right solution for the problem. What I bring to the table: 1. Practical decision-making: I’ve cut time‑to‑insight by up to 40% with lightweight models when they deliver results, and only scale to complex LLM or RAG workflows when they truly add value. 2. End-to-end ownership: I make sure prototypes thrive in production from data ingestion and feature engineering to Docker‑ized deployments on AWS and monitoring with MLOps best practices. 3. Balanced expertise: My expertise includes PyTorch, TensorFlow, FastAPI, Flask, prompt engineering, RAG, LLM observability, and traditional ML, providing you with solutions tailored to your business needs. I’m committed to solving real problems rather than just developing for the sake of it. Whether I’m using a simple model or an advanced AI system, my focus is on what truly benefits the business. If you're working on something meaningful and are looking for someone who thinks beyond the tools and aims for real impact, I’d love to connect.
• Engineered the AI/backend for HeyKyra (https://heykyra.ai/en) (proactive life OS handling 5K+ tasks/month) using FastAPI, LangChain, and LangGraph with voice (ElevenLabs) and integrations across Google Calendar, Apple Health, and Plaid. Resolved a critical RAG failure (temporal queries bypassing retrieval) via a metadata-aware tool router, and migrated vector search from ChromaDB to pgvector + DiskANN on Azure, removing single-node bottlenecks and improving retrieval at scale. • Fixed major HIPAA compliance gaps in a mental health platform by eliminating plaintext PII exposure and securing data flows. Implemented AWS KMS-based encryption across a NestJS + MongoDB stack, enabling safe clinical use and unblocking fundraising. • Built an NLP-to-XML agentic system that lets non-technical teams manage complex survey logic via plain English, reducing update time by 95% and removing engineering dependency. • Rebuilt two broken fintech MVPs into production systems: replaced mock data with live FRED APIs, secured the stack, restored real-time pricing + borrower-lender matching, and developed an ML-based rate decision engine for loan officers. • Developed a full SaaS platform for engineering plan analysis (Next.js + FastAPI), handling large PDF workflows asynchronously via queue-based architecture, eliminating timeouts under load. Integrated Stripe-based access control with automated result delivery. • Delivered additional systems including an AI health app (LLM-powered recommendations), a zero-touch news publishing pipeline, and OpenCV-based 3D apparel rendering. Standardized AWS infra + CI/CD pipelines, reducing deployment time by 80%+.
• Develop real-time anomaly detection and behaviour analysis models for high-volume log streams. • Build scalable feature engineering pipelines using Python, PyTorch, and scikit-learn. • Containerised ML services with Docker for seamless integration. • Implement MLOps pipelines with CI/CD, model versioning and real‑time monitoring to ensure production reliability and performance.