AI Engineer

Key JD requirements:

• Minimum 2 years in a similar role, with broad functional expertise (preferably retail) and technical expertise in AI/ML solution architecture
• 2+ years leading technical/software engineering teams or managing complex tech projects
• 2+ years hands-on developing AI/ML solutions and generative AI technologies/frameworks, with at least 2 years specifically in retail, e-commerce, or FMCG
• 2+ years large-scale backend system development experience
• Degree in Computer Science, IT, Programming & System Analysis, Computer Studies, or related discipline Agile certification a plus
• Business Central, Tableau, Microsoft Fabric, or Salesforce certification an advantage
• Proficiency in Python or R cloud data platforms (AWS, Azure, or GCP)
• Deep ML/Deep Learning/NLP/Computer Vision knowledge applied to retail (e.g. image search, sentiment analysis)
• Generative AI applications in retail (product description generation, virtual try-ons, personalized email copy)
• SQL and database schema design - relational and non-relational
• Cloud platforms (Azure/GCP), distributed computing, container orchestration (Docker, Kubernetes), MLOps tooling (MLflow, Kubeflow, Airflow)
• Familiarity with retail data structures - POS, ERP, CRM systems
• Strong commercial acumen tying AI metrics to retail KPIs (e.g. conversion rate) strong storytelling/communication to explain complex AI models to technical and non-technical stakeholders
• Available for weekend/off-hours on-site support and 24/7 call support

Scope of work:

• Omni-channel customer experience & personalization - AI-driven personalization engines, conversational AI for customer service, in-store layout optimization, customer behaviour tracking solution architecture, feature development, technical support customization incl. code structure, extension architecture, theming, caching, API integration (REST/SOAP)
• AI systems architecture - design/execute end-to-end platforms for advanced analytics and enterprise AI align AI solutions with technology architecture and business strategy strategic input on generative AI, LLMs, and ML frameworks
• Leadership & strategy - direct AI project management, solution delivery, vendor/supplier/contractor and internal IT workforce management evaluate and negotiate with third-party AI vendors and SaaS platforms
• Partnership - work with internal/external stakeholders and vendor management initiate/prioritize projects and process improvements ensure quality and ROI translate technical concepts for non-technical audiences
• Project & risk management - ensure deliverables on track and meet/exceed expectations enforce PM principles incl. risk management, prototyping, and PoC delivery for emerging tech
• Governance & security - enforce information security policy across IT functions periodic gap assessments ensure AI initiatives comply with data privacy regulations
• People & resource management - build a performance culture establish resource allocation/utilization controls
• Change management & service excellence - ensure solutions are tested and meet specs/timelines through deployment
• Other - monitor solution/project quality, contract & procurement management, IT policy development/review, audit support, ad-hoc tasks