The Role
Please ensure you read the below overview and requirements for this employment opportunity completely.
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, xphnsxz 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