About Me

I am Sheikh Rasel Ahmed, an AI/ML Engineer with 3+ years of experience designing, building, and deploying production-grade machine learning systems. I specialize in forecasting, feature engineering, and scalable ML pipelines, and I have hands-on experience with Python, Django REST Framework, TensorFlow, PyTorch, MLflow, ZenML, Docker, and AWS.

I have built end-to-end ML solutions, from data collection and preprocessing to model deployment and real-time APIs, improving system efficiency and business decision-making. My work spans real-time demand forecasting systems, ML-powered analytics platforms, and integration of ML workflows into production backends.

I am passionate about solving complex problems, learning new technologies, and turning data into actionable insights. Feel free to explore my projects and connect if you have collaboration opportunities.

Email: sheikhraselahmed19@gmail.com

Available: Remote or Fulltime

4

Years of

Experience

10

Happy

Clients

50

Complete

Projects

Technical Skills

Python (Advanced)

95%

Machine Learning & Forecasting

95%

Deep Learning (CNN, RNN, LSTM)

85%

NLP & LLMs (LangChain)

80%

Computer Vision (OpenCV)

90%

Django REST Framework & FastAPI

90%

MLOps (MLflow, ZenML)

95%

Docker & CI/CD for ML

90%

AWS (EC2, S3, RDS)

75%

HTML / CSS / JS

80%

Experience

Software Engineer (Backend – ML Integration)

Short Circuit Science, London, UK (Remote) | Oct 2024 – Dec 2025

Built and maintained scalable backend services to integrate machine learning workflows into production systems. Developed RESTful APIs using Django REST Framework to support real-time ML inference, analytics, and data-driven applications. Designed efficient database schemas and optimized queries to ensure system reliability, performance, and clean architecture across ML-enabled platforms.

Machine Learning Engineer

Jobdesk, Switzerland (Remote) | Oct 2022 – Sep 2023

Designed and deployed end-to-end machine learning pipelines covering data collection, feature engineering, model training, and cloud deployment. Processed and analyzed 50K+ records to build high-quality datasets and improve model performance. Implemented MLOps practices using MLflow and ZenML for experiment tracking, model versioning, and reproducible deployments on AWS.

Services

Machine Learning Solutions

Design, develop, and deploy custom machine learning models for real-time predictions, data analytics, and business intelligence applications.

Data Engineering & Pipelines

Build scalable data pipelines, automate feature engineering, and process large datasets efficiently for ML model training and analytics.

MLOps & Deployment

Implement CI/CD for machine learning, model versioning, automated experiment tracking, and deployment on cloud platforms like AWS using Docker, MLflow, and ZenML.

AI Integration & Backend Services

Integrate ML workflows into production systems using Django REST Framework and FastAPI, enabling real-time analytics, inference APIs, and automated decision-making.

Computer Vision Solutions

Develop computer vision systems for image and video analysis, including object detection, tracking, and performance analytics using OpenCV and deep learning models.

ML Consulting & Proof of Concept

Help businesses validate ideas through rapid ML proof-of-concepts, feasibility analysis, and model evaluation before full-scale production deployment.