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.

Key Responsibilities

· 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 BigQuery, 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, Data Scientists, Data Engineers, Cloud Architects and business stakeholders.

· Translate business requirements into technical AI/ML solutions and user-centric experiences.

· Lead technical discussions, AI solution workshops and design-thinking sessions with stakeholders.

· Communicate AI concepts, model outputs and technical considerations to both technical and non-technical audiences.

· Support client engagements, solution proposals, proof-of-concepts and AI transformation initiatives.

Qualifications & Experience

· Bachelor's in Computer Science, Information Technology, AI/ML or related fields.

· 3+ years of professional experience in AI/ML engineering, AI solution development or related AI engineering roles.

· Strong hands-on experience developing and deploying AI/ML solutions.

· Experience with data extraction, transformation, integration and processing for AI/ML applications.

· Strong programming/scripting skills in Python or equivalent programming languages.

· Strong understanding of machine learning, statistics, algorithms and model evaluation.

· Experience working with Generative AI, LLMs, embeddings and conversational AI.

· Hands-on experience with Google Cloud AI / Vertex AI or comparable enterprise AI/ML platforms.

· Experience with BigQuery, cloud data pipelines and analytics platforms.

· Understanding of MLOps, including model training, deployment, monitoring, versioning and lifecycle management.

· Experience troubleshooting and optimising deployed AI/ML models.

· Experience designing or contributing to AI-powered UX and enterprise digital experiences.

· Knowledge of UX principles, user research, prototyping and human-centred design is highly desirable.

· Experience with Figma or similar UX/prototyping tools is an advantage.

· Strong understanding of responsible AI, explainability, data privacy and AI governance.

· Excellent communication, stakeholder management and presentation skills.

· Experience working in Agile, Design Thinking or cross-functional product development environments.