1. Data Engineering & Pipeline Development

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Build reliable data ingestion and transformation workflows from structured and unstructured data sources.
• Develop data processing solutions using Python, SQL, Spark, or equivalent technologies.
• Perform data cleansing, transformation, validation, and quality checks.
• Optimise data pipelines for performance, scalability, reliability, and cost efficiency.
• Implement batch and real-time/streaming data processing where required.

2. Cloud Data Engineering

• Design and implement cloud-based data engineering solutions.
• Work with cloud data platforms such as:
  • Microsoft Azure
  • Amazon Web Services (AWS)
  • Google Cloud Platform (GCP)
• Develop and manage cloud-based data lakes, data warehouses, and data processing environments.
• Implement secure and scalable data storage and processing architectures.
• Monitor cloud data workloads and optimise resource utilisation and costs.
• Work with cloud-native data services and managed analytics platforms.

3. AI & Machine Learning

• Develop and deploy machine learning models for business and operational use cases.
• Prepare datasets for training, validation, and testing of ML models.
• Implement predictive analytics, classification, regression, clustering, and other ML techniques as applicable.
• Support development of AI-powered applications and intelligent automation solutions.
• Integrate ML models with enterprise applications and data platforms.
• Monitor model performance and support model improvement and retraining.

4. Generative AI & Modern AI Technologies

• Work with Generative AI, Large Language Models (LLMs), NLP, and AI-powered applications.
• Develop solutions using APIs and frameworks for modern AI/ML applications.
• Work with techniques such as:
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • Embeddings
  • Vector Databases
  • Document Processing
  • Natural Language Processing
• Integrate AI capabilities into existing enterprise applications and workflows.
• Evaluate AI models for accuracy, reliability, scalability, and practical business applicability.
  
  

  Machine Learning / AI

  • AWS Certified Machine Learning Engineer – Associate
  • Google Cloud Professional Machine Learning Engineer
  • Microsoft Certified: Azure AI Engineer Associate
  • TensorFlow Developer Certification or equivalent recognised ML certification
  • Other internationally recognised Machine Learning / Artificial Intelligence certification