AI Software Manager

Avesoro is looking for an experienced AI Software Manager to lead the design, development, implementation, and management of Artificial Intelligence solutions that support the company's digital transformation strategy. digital transformation strategy, and to build the organisational AI capability required for those solutions to be adopted and sustained across the business.

Key Responsibilities

• Develop and execute the company's Artificial Intelligence strategy and define the AI roadmap,
• Lead Artificial Intelligence projects from concept through deployment into production,
• Manage AI project budgets, timelines, resource planning, code quality, and risk management processes,
• Develop innovative AI-based solutions to improve efficiency across business processes,
• Drive the adoption and expansion of Artificial Intelligence technologies throughout corporate applications and operations,
• Analyze large datasets to generate business insights and strategic recommendations that create value for business processes,
• Monitor the accuracy, performance, and sustainability of AI models, ensuring continuous monitoring and retraining whenever necessary,
• Coordinate data collection, data cleansing, data labeling, and data governance processes,
• Establish, scale, and manage cloud-based or on-premise AI infrastructures,
• Develop AI architectures and standards that comply with cybersecurity, data privacy, and information security requirements,
• Establish AI governance across the portfolio: use case prioritisation, risk classification, approval gates, model documentation and audit trails, in line with EU AI Act, ISO/IEC 42001, KVKK and GDPR requirements across all operating jurisdictions,
• Promote AI awareness, organizational capabilities, and digital culture across the company,
• Design and run a group-wide AI literacy and competency assessment, establishing a baseline of current AI knowledge, skills and tool usage across functions, seniority levels and operating sites, and repeating it periodically to measure movement rather than treating it as a one-off exercise,
• Develop an AI competency framework mapping the required proficiency level for each role family (AI-aware, AI-enabled, AI-practitioner, AI-builder), and use it to identify capability gaps and prioritise intervention,
• Translate the gap analysis into a tiered enablement programme: executive briefings for senior leadership, applied role-specific training for operational and functional teams, and deep technical enablement for engineering and data staff,
• Define, track and report AI literacy and adoption KPIs (assessment coverage, proficiency shift over time, active tool usage, and the number and quality of employee-originated use cases) alongside project ROI in reporting to senior management,
• Establish responsible AI and acceptable-use guidance and embed it within the literacy programme, so that employees understand data confidentiality, verification of AI outputs, bias, and where human review is mandatory, particularly for safety-critical and financially material decisions,
• Build and support an internal network of AI champions across business units and operating sites, accounting for differences in connectivity, working language and local context at international operations,
• Run a structured intake and triage process for AI use cases originating from the business, converting them into a prioritised, feasibility-assessed portfolio rather than handling ad hoc requests,
• Partner with HR and Learning & Development to embed AI competencies into role descriptions, onboarding, performance frameworks and hiring criteria for AI-adjacent roles,
• Benchmark organisational AI maturity against recognised frameworks and industry peers annually, and set the following year's capability targets accordingly,
• Continuously monitor emerging Artificial Intelligence technologies, global industry trends, and scientific developments, integrating relevant innovations into the organization,
• Present regular project progress reports, performance analyses, and ROI evaluations to senior management,
• Measure the business impact of AI investments and recommend continuous improvement initiatives,
• Define and manage software development standards, code quality, testing methodologies, and documentation processes,
• Build, lead and develop the AI and data team [state expected size], and manage external partners, vendors, system integrators and academic collaborations,

Qualifications

Education

• Bachelor's degree in Computer Engineering, Software Engineering, Artificial Intelligence Engineering, Electrical & Electronics Engineering, Industrial Engineering, Computer Science or a related quantitative discipline.
• Master's degree or PhD i