Lead Consultant

Job Description · 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, m…