Machine Learning Engineer

We are seeking a Machine Learning Engineer to design and deploy next-generation AI and machine learning solutions at scale. This role focuses on building production-ready models, robust ML pipelines, and modern AI capabilities that translate business needs into real-world impact.

Key Responsibilities

• Design, develop, and deploy machine learning models and AI systems into production
• Build and maintain scalable ML pipelines covering data ingestion, training, evaluation, deployment, and monitoring
• Collaborate with cross-functional teams to translate business requirements into AI/ML solutions
• Optimise models and systems for performance, scalability, and reliability in production environments
• Implement MLOps best practices including CI/CD, model versioning, experiment tracking, and automated retraining
• Monitor and maintain model performance, including handling drift and system reliability
• Develop and integrate AI capabilities across domains such as computer vision, natural language processing, and modern approaches including generative AI or agent-based systems where applicable
• Ensure adherence to data governance, security, and best engineering practices

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
• At least 3 years of experience in machine learning engineering, AI engineering, or related roles
• Strong programming and software engineering skills
• Hands-on experience with machine learning and modern AI/agentic frameworks (e.g., PyTorch, scikit-learn, LangChain, or similar)
• Understanding of the end-to-end machine learning lifecycle, including data preparation, model development, evaluation, deployment, and monitoring
• Understanding of software engineering best practices (testing, version control, CI/CD)
• Familiarity with a range of machine learning techniques across domains such as computer vision, natural language processing, and/or generative AI
• Experience with data processing tools and large-scale data systems
• Experience building and deploying machine learning models in production environments
• Experience deploying applications using APIs, containers, and orchestration tools
• Familiarity with cloud platforms (AWS, Azure, or GCP)