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Muhammad Ibrahim
Verified Expert in Engineering
Python Developer
Lahore, Punjab, Pakistan
Toptal member since February 24, 2026
Bio
Muhammad is a senior machine learning engineer with 7+ years of experience in AI/ML, MLOps, and scalable system design. He focuses on NLP, ASR, computer vision, and distributed training of transformer models, including LLMs and GenAI. With a strong background in CI/CD, TDD/BDD, and Agile practices, he's proficient in Python, PyTorch, TensorFlow, Django, AWS, and Kubernetes. Muhammad leads AI solutions from research to production, optimizing performance and scalability.
Portfolio
Devsinc
Artificial Intelligence (AI), Machine Learning, Data Science...
Devsinc
Agentic AI, Agentic RAG Systems, AI Tools, REST APIs, Stripe, CI/CD Pipelines...
Aventex Labs
Artificial Intelligence (AI), Machine Learning, Google Cloud Platform (GCP)...
Experience
- Python - 9 years
- Artificial Intelligence (AI) - 6 years
- Kubernetes - 4 years
- Machine Learning - 4 years
- Large Language Models (LLMs) - 4 years
- Prompt Engineering - 4 years
- Retrieval-augmented Generation (RAG) - 3 years
- Amazon Bedrock AgentCore - 2 years
Preferred Environment
Python
The most amazing...
...thing I've developed is a dynamic visualization platform that was deployed across multiple universities and colleges
Work Experience
Senior AI/ML Engineer
2023 - 2026
Devsinc
- Architected and deployed an end-to-end real-time voice AI pipeline (LLM, guardrails, STT, and TTS), reducing latency and scaling to thousands of concurrent users using Kubernetes and Docker.
- Built a healthcare ML system to monitor glucose levels and predict glucose spikes, enabling proactive interventions through predictive modeling and real-time analytics.
- Developed agentic AI frameworks leveraging Stable Diffusion to automate design ideation workflows, accelerating concept generation for interior and spatial designers.
- Engineered a dynamic real estate intelligence platform processing millions of US property listings, enabling structured query-based search and large-scale data retrieval.
- Designed and deployed a fine-tuned LLM-powered educational platform that delivered adaptive learning experiences and scaled across multiple universities.
- Designed and implemented PHI/PII redaction pipelines (text and audio) using ensemble models and rule-based validators, ensuring 100% compliance with HIPAA and enterprise data-governance policies.
- Integrated long-term memory into agents using optimized vector stores (Weaviate and FAISS), embedding compression, and approximate nearest neighbor search.
- Created automated testing frameworks to evaluate LLM performance, ensuring robustness, reliability, and efficiency across diverse domains.
Technologies: Artificial Intelligence (AI), Machine Learning, Data Science, Machine Learning Operations (MLOps), LangChain, LangGraph, Amazon Bedrock AgentCore, Google Cloud Platform (GCP), Azure, Amazon Web Services (AWS), Docker, Kubernetes, AutoGen, OpenAI, Claude, Amazon DynamoDB, Amazon SageMaker, PostgreSQL, Django, FastAPI, MLflow, Retrieval-augmented Generation (RAG), API Development, Natural Language Processing (NLP), APIs, Generative Pre-trained Transformers (GPT), Identity & Access Management (IAM), Data Privacy, Personally Identifiable Information (PII), Vector Databases, LlamaIndex, AI Agents, Agentic RAG Systems, Gemini API, Agentic AI, Prompt Engineering, Large Language Models (LLMs), Agentic Frameworks, AI Programming, AI Tools, AI Voice Agents, Vapi, n8n, JavaScript, RAG Systems, Security, Technical Leadership, DevOps, Multi-agent Systems, API Integration, Model Context Protocol (MCP), REST APIs, Stripe, System Design, CI/CD Pipelines, Supabase, AWS IoT, Amazon ElastiCache, LangSmith, OpenAI API, Pinecone, Redis, Scalable Vector Databases, React, AI Automation, AI Consulting, RAG Architecture, Anthropic, Generative Artificial Intelligence (GenAI), Vertex AI, OpenAI GPT-4 API, Benchmarking, Hyperparameter Tuning, Optical Character Recognition (OCR), Rule-based Programming
Machine Learning Engineer
2021 - 2023
Devsinc
- Engineered and deployed robust ML models for predictive analytics, classification, and recommendation systems, reducing error rates by 15% and increasing decision-making efficiency by 20%.
- Architected end-to-end data pipelines for large-scale datasets, optimizing feature extraction, transformation, and storage to boost model accuracy by 12% and cut data processing times by 30%.
- Applied advanced model optimization techniques, including pruning, quantization, and feature selection, to reduce inference times by 40% and lower computational costs by 35%.
- Introduced new ML-driven health metrics to monitor glucose trends and variability for users, leveraging time-series modeling and anomaly detection.
Technologies: Agentic AI, Agentic RAG Systems, AI Tools, REST APIs, Stripe, CI/CD Pipelines, Supabase, AWS IoT, Amazon ElastiCache, LangSmith, OpenAI API, Pinecone, Redis, Scalable Vector Databases, React, AI Automation, AI Consulting, RAG Architecture, OpenAI, Vertex AI, Benchmarking, Optical Character Recognition (OCR), Rule-based Programming
Machine Learning Engineer
2018 - 2021
Aventex Labs
- Assisted in developing predictive models for risk assessment and customer segmentation.
- Conducted data cleaning, feature engineering, and exploratory analysis on large insurance datasets.
- Implemented and evaluated machine learning algorithms under senior guidance, optimizing model performance.
- Supported the automation of data pipelines and training processes to streamline workflows.
Technologies: Artificial Intelligence (AI), Machine Learning, Google Cloud Platform (GCP), Azure, Amazon Web Services (AWS), Docker, Kubernetes, PostgreSQL, FastAPI, API Development, Natural Language Processing (NLP), APIs, Data Privacy, Identity & Access Management (IAM), Vector Databases, AI Programming, JavaScript, API Integration, REST APIs, AWS IoT, Redis, AI Automation, AI Consulting, Benchmarking, Rule-based Programming
Experience
Hermetic AI
http://www.hermetic.aiHermetic.ai is an AI-driven sales automation platform that instantly engages, qualifies, and converts inbound leads through intelligent, real-time SMS conversations. I contributed to the architecture and production deployment of its core conversational AI system, building scalable LLM-driven workflows that enabled automated lead qualification, routing, and appointment scheduling.
I designed low-latency inference pipelines to ensure near-instant responses, optimized prompt engineering to improve qualification accuracy, and implemented safety guardrails to mitigate risks of prompt injection and sensitive data exposure. I also integrated CRM systems, calendar APIs, and business-specific routing logic to support seamless end-to-end automation.
The platform was containerized and deployed using Kubernetes-based infrastructure to support concurrent conversations across multiple clients while maintaining reliability, observability, and horizontal scalability. My work helped transition the system from prototype to a stable, production-grade AI solution capable of handling real-world demand.
I designed low-latency inference pipelines to ensure near-instant responses, optimized prompt engineering to improve qualification accuracy, and implemented safety guardrails to mitigate risks of prompt injection and sensitive data exposure. I also integrated CRM systems, calendar APIs, and business-specific routing logic to support seamless end-to-end automation.
The platform was containerized and deployed using Kubernetes-based infrastructure to support concurrent conversations across multiple clients while maintaining reliability, observability, and horizontal scalability. My work helped transition the system from prototype to a stable, production-grade AI solution capable of handling real-world demand.
AreaHub
AreaHub is an AI-powered real estate platform that processes dynamic user queries and returns relevant property listings across the United States. I led the architecture and development of the intelligent query system, building a text-to-SQL pipeline that translated natural-language inputs into optimized database queries against large-scale property datasets.
I designed scalable back-end services to handle high-volume search requests, implemented data normalization and indexing to improve retrieval efficiency, and optimized response latency for real-time user interaction. The platform supported millions of property records and enabled structured filtering, market insights, and data-driven property discovery. Additionally, I integrated visualization components to provide users with actionable insights and comparative analytics. My work ensured production-grade reliability, scalability, and performance, transforming complex real estate data into an intuitive, AI-driven search experience.
I designed scalable back-end services to handle high-volume search requests, implemented data normalization and indexing to improve retrieval efficiency, and optimized response latency for real-time user interaction. The platform supported millions of property records and enabled structured filtering, market insights, and data-driven property discovery. Additionally, I integrated visualization components to provide users with actionable insights and comparative analytics. My work ensured production-grade reliability, scalability, and performance, transforming complex real estate data into an intuitive, AI-driven search experience.
Healthcare Chatbot
https://healthanswers.pfizer.comAt Health Answers by Pfizer, I played a key role in designing and building a multi-agent conversational AI framework that powers scalable, domain-aware interactions across a wide range of healthcare use cases. My work focused on orchestrating multiple specialized AI agents that collaborate intelligently to understand user intent, retrieve accurate medical and pharmaceutical information, and generate safe, compliant responses. This involved integrating secure RAG pipelines with domain-specific knowledge sources, implementing vector-based retrieval with identity-aware access controls, and layering safety guardrails to prevent prompt injection and ensure medical content accuracy.
Education
2014 - 2018
Bachelor's Degree in Computer Science
National University of Computer and Emerging Sciences - Lahore, Pakistan
Skills
Libraries/APIs
OpenAI API, API Development, REST APIs, Stripe, React
Tools
Claude, Amazon SageMaker, n8n, Amazon ElastiCache
Languages
Python, JavaScript
Frameworks
LangGraph, AutoGen, Django, LlamaIndex, Agentic Frameworks
Platforms
Docker, Kubernetes, Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP), AWS IoT, LangSmith, Vertex AI
Storage
Redis, Amazon DynamoDB, PostgreSQL
Paradigms
DevOps, Model Context Protocol (MCP), Rule-based Programming
Other
Artificial Intelligence (AI), Machine Learning, Large Language Models (LLMs), Prompt Engineering, Amazon Bedrock AgentCore, LangChain, OpenAI, Generative Pre-trained Transformers (GPT), Retrieval-augmented Generation (RAG), AI Agents, Agentic AI, AI Voice Agents, Vapi, RAG Systems, Multi-agent Systems, Anthropic, Generative Artificial Intelligence (GenAI), OpenAI GPT-4 API, Machine Learning Operations (MLOps), Data Science, FastAPI, MLflow, APIs, Natural Language Processing (NLP), Vector Databases, Personally Identifiable Information (PII), Identity & Access Management (IAM), Data Privacy, Agentic RAG Systems, Gemini API, AI Programming, AI Tools, Security, Technical Leadership, API Integration, System Design, CI/CD Pipelines, Supabase, Pinecone, Scalable Vector Databases, AI Automation, AI Consulting, RAG Architecture, Benchmarking, Hyperparameter Tuning, Optical Character Recognition (OCR)
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