Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I'm an ML Engineer with six years of experience, evolving from Python development to cutting-edge machine learning and generative AI. I build real-world LLM applications, with hands-on work in prompt tuning, retrieval-augmented generation, and scalable inference pipelines. I also help shape ML architecture for enterprise deployment, optimizing models for reliability and security, and I thrive in remote, flexible teams. I enjoy collaborating with product, design, and QA to deliver polished AI experiences and to improve UX through iterative feedback.…I'm an ML Engineer with six years of experience, evolving from Python development to cutting-edge machine learning and generative AI. I build real-world LLM applications, with hands-on work in prompt tuning, retrieval-augmented generation, and scalable inference pipelines. I also help shape ML architecture for enterprise deployment, optimizing models for reliability and security, and I thrive in remote, flexible teams. I enjoy collaborating with product, design, and QA to deliver polished AI experiences and to improve UX through iterative feedback.

Mohammed Suleman

AI Engineer, Data Scientist, Full Stack Developer, +4





I'm an ML Engineer with six years of experience, evolving from Python development to cutting-edge machine learning and generative AI. I build real-world LLM applications, with hands-on work in prompt tuning, retrieval-augmented generation, and scalable inference pipelines. I also help shape ML architecture for enterprise deployment, optimizing models for reliability and security, and I thrive in remote, flexible teams. I enjoy collaborating with product, design, and QA to deliver polished AI experiences and to improve UX through iterative feedback.…I'm an ML Engineer with six years of experience, evolving from Python development to cutting-edge machine learning and generative AI. I build real-world LLM applications, with hands-on work in prompt tuning, retrieval-augmented generation, and scalable inference pipelines. I also help shape ML architecture for enterprise deployment, optimizing models for reliability and security, and I thrive in remote, flexible teams. I enjoy collaborating with product, design, and QA to deliver polished AI experiences and to improve UX through iterative feedback.

Available to hire

I’m an ML Engineer with six years of experience, evolving from Python development to cutting-edge machine learning and generative AI. I build real-world LLM applications, with hands-on work in prompt tuning, retrieval-augmented generation, and scalable inference pipelines.

I also help shape ML architecture for enterprise deployment, optimizing models for reliability and security, and I thrive in remote, flexible teams. I enjoy collaborating with product, design, and QA to deliver polished AI experiences and to improve UX through iterative feedback.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Gen AI Engineer at Glean

January 1, 2024 - Present

Designed and deployed RAG pipelines with LangChain and Pinecone to deliver accurate, context-aware enterprise search. Tuned prompts for GPT-4 and Claude to improve response quality and reduce hallucinations. Integrated Google Drive, Slack, and Notion via secure APIs to extend assistant functionality. Containerized workflows with Docker and Azure Functions for scalable inference. Built an evaluation framework to benchmark OpenAI, Anthropic, and LLaMA-2 models for production readiness and integrated AWS Bedrock for deploying and fine-tuning foundation models. Developed secure API workflows to connect on-premise systems with Bedrock, ensuring compliance and optimized performance. Collaborated with design, product, and QA teams to refine UX and performance. Implemented ML pipelines with MLflow and CI/CD, and monitored data drift and model reliability. Used TensorFlow for model training and benchmarking.

ML Engineer at MAD (Mad Street Den)

November 1, 2023 - October 22, 2025

Built ML models using scikit-learn and XGBoost to categorize customer reviews; applied NLTK and SpaCy for text preprocessing, NER, and sentiment tagging; created LDA-based topic modeling to surface insights; automated data ingestion and cleaning with Python and Airflow. Delivered outputs to dashboards via Flask and lightweight REST APIs; collaborated with DevOps for deployment on AWS EC2 and S3.

Python Developer at Scribble Data

December 1, 2021 - October 22, 2025

Wrote data ingestion scripts and API connectors using Python (FastAPI, Pandas). Built data profiling tools to identify anomalies before pushing into model pipelines; implemented logging and alerting to catch pipeline failures early; created unit tests and CI workflows with GitHub Actions. Containerized ETL tools with Docker to ensure reproducibility across environments.

Gen AI Engineer at Glean

January 1, 2024 - November 11, 2025

Designed and deployed retrieval-augmented generation (RAG) pipelines with LangChain and Pinecone to deliver context-aware enterprise search. Integrated enterprise platforms (Google Drive, Slack, Notion) via secure APIs to extend assistant capabilities. Developed an evaluation framework to benchmark OpenAI, Anthropic, and LLaMA-2 models for production readiness. Optimized and containerized workflows with Docker and Azure Functions for scalable inference. Leveraged TensorFlow for model training and benchmarking, feeding results into the LLM evaluation framework. Enhanced generative AI capabilities by integrating AWS Bedrock for deploying and fine-tuning foundation models. Built secure API workflows connecting on-premise systems with Bedrock to meet compliance and performance needs. Collaborated with design, product, and QA teams to refine assistant behavior and UX through iterative feedback. Implemented end-to-end ML pipelines with MLflow and CI/CD, and established monitoring for data dr

ML Engineer at MAD (Mad Street Den)

November 1, 2023 - November 1, 2023

Developed ML models to categorize customer reviews using scikit-learn and XGBoost. Performed text preprocessing with NLTK and SpaCy, and engineered a topic modeling pipeline using LDA to surface insights. Automated data ingestion and cleaning with Python and Airflow. Collaborated with product analysts to validate outputs and improve model confidence. Integrated model outputs into dashboards via Flask-based REST APIs and deployed solutions on AWS EC2/S3.

Python Developer at Scribble Data

December 1, 2021 - December 1, 2021

Authored data ingestion scripts and API connectors using Python (FastAPI, Pandas). Built data profiling tools to identify anomalies prior to integration into model pipelines. Implemented logging and alerting features to capture pipeline failures promptly. Created unit tests and CI workflows using GitHub Actions. Containerized ETL tools with Docker to ensure reproducibility across environments. Engaged with clients to gather requirements and support seamless integrations, aligning data formats with model training expectations.