ML Engineer (AI/ML Lab)

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
• 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)