Machine Learning Engineer

The Role

As a Machine Learning Engineer, you will be responsible for the ML lifecycle—from data ingestion and model development to deployment and monitoring in production. You will be working on real-world, large-scale challenges, applying strong engineering practices to build reliable machine learning systems.

Responsibilities

• Design, develop, and deploy machine learning models for production use cases (e.g., recommendation systems, NLP, computer vision, predictive analytics)
• Build and maintain scalable ML pipelines for training, evaluation, and inference
• Work with both structured and unstructured data across diverse domains
• Implement robust data preprocessing, feature engineering, and transformation workflows
• Ensure data quality, integrity, and compliance with data governance standards (e.g., GDPR)
• Optimize models for performance, scalability, and cost-efficiency in production environments
• Collaborate with data engineers, software engineers, and product stakeholders
• Deploy and manage models using cloud platforms (AWS, Azure, or GCP) and containerization tools
• Implement monitoring, validation, and testing frameworks to ensure model reliability
• Continuously improve model performance through experimentation, iteration, and validation
• Contribute to MLOps practices, including CI/CD pipelines, model versioning, and reproducibility

Your Profile

• 3–6+ years of experience in Machine Learning Engineering, AI Engineering, or related roles
• Strong programming skills in Python
• Hands-on experience with ML/DL frameworks (e.g., TensorFlow, PyTorch)
• Solid understanding of machine learning algorithms, model evaluation, and optimization techniques
• Proven experience building and deploying ML pipelines in production environments
• Familiarity with data engineering concepts (ETL/ELT, data pipelines)
• Experience with cloud platforms (AWS, Azure, or GCP)
• Experience with containerization tools (Docker, Kubernetes) is a plus
• Understanding of MLOps practices and tools (e.g., MLflow, Airflow)
• Experience working with large-scale or complex datasets
• Awareness of data privacy and governance best practices

The Offer

• Competitive salary and comprehensive benefits package
• Hybrid working environment
• Opportunity to work on high-impact, scalable ML systems in a modern tech environment
• Route for growth within the company

Apply

If this opportunity excites you, apply today or send your CV and a short cover letter to [email protected]