Lead Consultant

We are looking for an AI Engineer with strong expertise in Artificial Intelligence, Machine Learning, Generative AI, Google Cloud and AI-powered User Experience (UX) to design, develop and deliver scalable enterprise AI solutions.

The role combines hands-on AI/ML engineering with AI/UX design, working closely with Data Scientists, ML Engineers, UX Designers, Product Managers, Cloud Architects and business stakeholders to build intelligent, user-centric and enterprise-ready solutions.

• AI/ML Engineering & Solution Development Design, develop and support scalable and optimised AI/ML solutions for enterprise use cases.
• Develop and implement AI/ML algorithms and solutions based on business and user requirements.
• Build data processing workflows for extracting, transforming and loading large volumes of structured and unstructured data.
• Prepare, integrate and transform data required for AI/ML model development and deployment.
• Work with Data Scientists and ML Engineers to convert analytical and theoretical models into production-ready AI solutions.
• Conduct experiments to evaluate model performance, accuracy, scalability and reliability.
• Identify, troubleshoot and resolve issues affecting deployed AI/ML solutions.
• Apply statistics, scripting and programming skills to develop and optimise AI solutions.
• Work with relevant software platforms and cloud environments where AI/ML models are developed and deployed.
• Google Cloud AI & Generative AI Design and deliver AI solutions using Google Cloud AI services, including Vertex AI and managed ML services.
• Develop enterprise use cases using Generative AI, LLMs, foundation models, embeddings and conversational AI.
• Design AI/ML pipelines covering data ingestion, processing, model development, testing, deployment and monitoring.
• Work with Big Query, data pipelines and Google Cloud analytics services to support AI/ML workflows.
• Integrate Google AI APIs for use cases including Natural Language, Vision, Speech, Search and Recommendations.
• Evaluate and experiment with emerging Google AI capabilities and translate them into practical enterprise solutions.
• Balance custom AI/ML development with managed Google Cloud services based on performance, scalability, cost and business requirements.
• MLOps & Model Lifecycle Support model training, validation, deployment, monitoring and lifecycle management.
• Implement appropriate MLOps practices for model versioning, deployment and monitoring.
• Monitor deployed models and identify opportunities for performance optimisation.
• Support continuous improvement of AI/ML solutions based on model performance and user feedback.
• Ensure AI solutions are scalable, reliable and suitable for enterprise production environments.
• AI/UX & User Experience Design AI-powered experiences that translate complex AI capabilities and model outputs into intuitive user interactions.
• Work with UX teams to integrate AI capabilities across web, mobile, conversational interfaces, dashboards and enterprise applications.
• Design interfaces that allow users to understand, validate and act on AI-generated insights.
• Develop explainable and transparent AI experiences that improve user trust and adoption.
• Design human-in-the-loop workflows where users can review, challenge and provide feedback on AI outputs.
• Contribute to user research, journey mapping, prototyping and usability testing for AI-powered products. Translate user needs and business requirements into practical AI solution designs.
• Data, Analytics & AI Insights Work with Data Scientists, Data Engineers and Analytics teams to prepare and utilise data for AI solutions.
• Analyse complex datasets and AI outputs to identify actionable business insights.
• Define and track AI solution KPIs including model performance, adoption, accuracy, user engagement and business impact.
• Support experimentation and A/B testing to evaluate AI features and user experiences.
• Assess data quality, bias and representativeness to support reliable AI outcomes.
• Responsible AI & Governance Apply responsible AI principles throughout the design and development lifecycle.
• Identify and address risks relating to bias, fairness, privacy, security, transparency and explainability.
• Support responsible use of enterprise data in AI/ML solutions.
• Ensure AI solutions align with applicable data protection, security and regulatory requirements.
• Promote transparent and human-centred AI design practices.
• Stakeholder & Client Collaboration Collaborate with Product Managers, UX Designers,…