Manager - AI Solutions Development & Operations

Job purpose

Strategy and vendor management

• Oversee implementation of the AI and data science roadmap against timelines, standards, and strategic objectives.
• Manage AI provider selection, qualification, onboarding, operational readiness, access, and data/platform integration prerequisites.
• Evaluate technical partnerships and recommend future collaborations and capability investments.
• Align AI solutions with digital transformation; prioritize use cases and investments using feasibility and impact assessments and horizon scanning.
• Identify and evaluate AI and big-data use cases with planning teams and business units.
• Monitor vendor service levels, contractual KPIs, and operational requirements; initiate corrective and improvement actions.

AI operations and lifecycle

• Manage enterprise AI solution lifecycle from design and development through testing, deployment, versioning, updates, and improvement.
• Coordinate service scope, SLAs, performance indicators, business continuity, support, team/vendor integration, handover, and knowledge transfer.
• Coordinate maintenance, releases, operational issues, incidents, changes, configurations, environment transitions, rollback, and production handover.
• Manage model versioning, retraining triggers, revalidation, material changes, updates, replacement, and decommissioning under governance and quality standards.
• Prepare implementation documentation, technical/data requirements, work plans, methodologies, and acceptance criteria.
• Coordinate UAT with business owners, prepare test scenarios, resolve defects and observations, and obtain sign-off.
• Provide specialized technical support, incident management, approved changes, user training, and adoption support.
• Maintain technical documentation, code repositories, models, reusable assets, and operational runbooks.

Development and improvement

• Design, develop, configure, and integrate AI, data science, and advanced analytics solutions in line with requirements, architectures, platforms, and technology standards.
• Perform exploratory data analysis, feature engineering, model training, tuning, experimentation, and technical evaluation using repeatable practices.
• Coordinate with platform and infrastructure authorities on requirements without assuming ownership of core enterprise infrastructure.
• Use user feedback to improve AI applications and implement acceptance and operational testing before production release.
• Monitor and report solution performance, model drift, data quality, costs, incidents, usage, user experience, and realized benefits; recommend corrective actions.

Qualifications

• Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a closely related technical field from an accredited university.
• Experience is written as “1” followed by “2 years in case of Bachelor, 14 years in case of Master”; the duration is malformed and unclear.
• Proven experience managing full AI/ML solution lifecycles from concept through production operations.
• Demonstrated expertise in vendor management, SLA governance, and technical procurement for AI services.
• Strong command of MLOps, model lifecycle management, and AI governance frameworks.