Manager - AI Solutions Development & Operations

Job Purpose :Lead the end-to-end development, deployment, and operational management of enterprise AI and data science solutions for RTA. Oversee vendor partnerships, model lifecycle management, and cross-functional integration to ensure secure, high-performance AI delivery. Drive continuous improvement, stakeholder alignment, and realization of strategic AI value across the organization.

Roles and Responsibilities

StrategyOversee the implementation of the AI and data science project roadmap, ensuring adherence to approved timelines, standards, and strategic objectives to maximize RTA's AI capability and competitive advantage.Manage the vendor selection, qualification, and onboarding process for AI solution providers, includingcoordination of operational requirements, access provisioning, data/platform readiness, and integration prerequisites to ensure vendor readiness for service delivery.Oversee the evaluation of technical partnerships with AI solution providers and technology vendors, providing strategic recommendations for future collaborations and capability investments.Drive strategic alignment of AI solutions with RTA's digital transformation agenda, ensuring that use case prioritization and investment decisions are grounded in feasibility studies, impact assessments, and horizon scanning of emerging AI technologies.Manage the identification and evaluation of appropriate AI and big data use cases to ensure the highes added value for RTA, in coordination with the AI Planning Section and relevant business units.Operations:Manage the full lifecycle of internally developed AI solutions at the enterprise level, encompassing design, development, testing, deployment, versioning, updates, and continuous improvement, in coordination with relevant stakeholders and within approved frameworks and standards.Oversee vendor performance management, monitoring compliance with service levels, contractual KPIs, and operational requirements initiate corrective and improvement actions to address deviations and ensure service continuity and target performance.Manage operational requirements for enterprise AI services and solutions, including service scope, SLA definitions, performance indicators, business continuity, and support arrangements, in coordination with the AI Planning Section and concerned parties.Ensure coordinate integration between internal development teams and external vendors, organizing delivery/handover processes, knowledge transfer, and transitions between delivery and operational models to ensure service continuity and preservation of technical capabilities.Manage the operational execution and maintenance of enterprise AI solutions, including version/release management, operational issue resolution, incident and change coordination, in integration with platform, data, infrastructure, vendor, and stakeholder teams.Oversee the controlled and secure deployment of models and applications, managing releases, configurations, environment transitions, rollback mechanisms, and production handover in accordance with approved change management procedures.Manage the model lifecycle, including versioning, retraining triggers, re-validation, material changes, updates, replacement, and decommissioning as required, ensuring compliance with governance and quality standards.Prepare comprehensive implementation documentation for approved AI and data products, including technical/data requirements, execution methodology, work plans, and technical acceptance criteria.Ensure coordinate with business owners to execute User Acceptance Testing (UAT), preparing test scenarios and documentation, and addressing observations/defects until final sign-off is obtained.Provide specialized technical support, incident management, and execution of approved changes on AI solutions in accordance with service levels and operational controls.Deliver user training and support, manage feedback and communications related to AI solutions to ensure effective adoption and proper utilization.Monitor usage metrics, user experience, realized outcomes, and benefits, providing data and evidence to support value measurement and scaling of approved solutions.Maintain and update technical documentation, code repositories, models, reusable assets, and operational runbooks, supporting knowledge transfer and final delivery/exit as needed.Product/Process ImprovementManage, design, develop, configure, and integrate AI, data science, and advanced analytics solutions in accordance with approved requirements, architectures, and platforms, ensuring alignment with RTA's technology standards.Ensure execute exploratory data analysis, feature engineering, model training, tuning, experimentation, and technical evaluation following documented and repeatable practices.Develop and operate solutions within approved environments, platforms, and data ecosystems, escalating platform or infrastructure requirements to the relevant authorities without assuming ownership of core enterprise infrastructure.Oversee the analysis of user feedback and suggestions, utilizing insights to improve the effectiveness and performance of operational AI applications and drive iterative enhancement.Ensure the implementation of acceptance and operational testing (UAT) mechanisms to verify that AI solutions comply with RTA's technical and operational requirements before production release.Monitor and report on AI solution performance, model drift, data quality issues, cost efficiency, and operational incidents, preparing periodic operational reports and recommending corrective actions.

Qualifications

Education: Bachelor or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a closely related technical field from an accredited universityExperience: 12 years in case of Bachelor, 14 years in case of MasterQualification:Proven track record in managing the full lifecycle of AI/ML solutions from concept through production operations; Demonstrated expertise in vendor management, SLA governance, and technical procurement for AI services; Strong command of MLOps practices, model lifecycle management, and AI governance frameworks