Machine Learning Engineer (AI/ML Lab)
Summary
Design and deploy production-grade AI/ML models and pipelines for airport operations, focusing on computer vision, NLP, and generative AI systems.
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)