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 architecture2+ years leading technical/software engineering teams or managing complex tech projects2+ years hands-on developing AI/ML solutions and generative AI technologies/frameworks, with at least 2 years specifically in retail, e-commerce, or FMCG2+ years large-scale backend system development experienceDegree in Computer Science, IT, Programming & System Analysis, Computer Studies, or related discipline; Agile certification a plusBusiness Central, Tableau, Microsoft Fabric, or Salesforce certification an advantageProficiency 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-relationalCloud platforms (Azure/GCP), distributed computing, container orchestration (Docker, Kubernetes), MLOps tooling (MLflow, Kubeflow, Airflow)Familiarity with retail data structures — POS, ERP, CRM systemsStrong 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 stakeholdersAvailable for weekend/off-hours on-site support and 24/7 call supportScope 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 frameworksLeadership & 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 platformsPartnership — work with internal/external stakeholders and vendor management; initiate/prioritize projects and process improvements; ensure quality and ROI; translate technical concepts for non-technical audiencesProject & risk management — ensure deliverables on track and meet/exceed expectations; enforce PM principles incl. risk management, prototyping, and PoC delivery for emerging techGovernance & security — enforce information security policy across IT functions; periodic gap assessments; ensure AI initiatives comply with data privacy regulationsPeople & resource management — build a performance culture; establish resource allocation/utilization controlsChange management & service excellence — ensure solutions are tested and meet specs/timelines through deploymentOther — monitor solution/project quality, contract & procurement management, IT policy development/review, audit support, ad-hoc tasks