The Role
You can get further details about the nature of this opening, and what is expected from applicants, by reading the below.
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, xlxgzvr 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
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