The Role
Not sure what skills you will need for this opportunity Simply read the full description below to get a complete picture of candidate requirements.
As a Machine Learning Engineer, you will be in charge of model development to deployment and production monitoring. You will be challenged with real-world, large-scale challenges whilst applying solid engineering principles to build robust, scalable, and reliable machine learning systems.
Key Responsibilities
* Design, build, and deploy machine learning models for production use cases such as recommendation systems, NLP, computer vision, and predictive analytics
* Develop and maintain scalable pipelines for training, evaluation, and inference
* Work with both structured and unstructured data across a variety of domains
* Implement efficient data preprocessing, feature engineering, and transformation workflows
* Ensure data quality, integrity, and compliance with governance standards (e.g., GDPR)
* Optimize models for performance, scalability, and cost-efficiency in production
* Collaborate closely with data engineers, software engineers, and product stakeholders
* Deploy and manage models using cloud platforms (AWS, Azure, or GCP) and containerization tools
* Build monitoring, validation, and testing frameworks to ensure model reliability
* Continuously improve model performance through experimentation and iteration
* Contribute to MLOps practices, including CI/CD, model versioning, and reproducibility
Your Profile
* 3–5+ years of experience in Machine Learning Engineering, AI Engineering, or a related field
* Strong programming skills in Python
* Hands-on experience with ML/DL frameworks such as TensorFlow or PyTorch
* Solid understanding of machine learning algorithms, evaluation methods, 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 xphnsxz with containerization tools (Docker, Kubernetes)
* Understanding of MLOps tools and practices (e.g., MLflow, Airflow)
* Experience working with large-scale or complex datasets
* Awareness of data privacy and governance best practices
What’s Offered
* Competitive salary and comprehensive benefits package
* Hybrid working environment
* Opportunity to work on impactful, scalable ML systems in a modern tech setting
* Clear path for professional growth and development
Apply
If this opportunity interests you, apply now or send your CV along with a short cover letter to .