AI Software Manager

Avesoro; Fatih; On-site; TRY 1,800,000 - 2,800,000; Full time

Job summary

Avesoro is seeking an experienced AI Software Manager to lead the design, development, implementation and ongoing management of enterprise AI solutions supporting the company’s digital transformation. You will build the AI capability across the organization and ensure adoption across business units.

You will oversee AI strategy, governance, data management, and cloud/on‑prem architectures while coordinating with HR, IT, and operations to enable scalable, secure AI deployments with measurable ROI.

Qualifications

• Bachelor's degree in a quantitative discipline with strong delivery record.
• Advanced degree preferred (MS/PhD) in CS/AI/ML/Data Science.
• Experience in cloud AI/ML governance and project management.

Responsibilities

• Define and execute the AI strategy and roadmap.
• Lead AI projects from concept to production with measurable impact.
• Manage AI budgets, timelines and risk management.
• Develop AI-based solutions to improve efficiency across processes.
• Drive adoption of AI technologies across applications.
• Monitor AI model performance and retrain as needed.
• Coordinate data collection, governance and labeling processes.
• Establish scalable AI architectures and security controls.

Skills

AI strategy

Team leadership

Budget management

Data governance

Cloud architectures

MLOps

Python

Data analysis

Stakeholder management

Cybersecurity

Education

Bachelor's degree in a related quantitative field

Master's degree or PhD preferred

Equivalent demonstrated experience

Tools

AWS

Azure

GCP

Docker

Kubernetes

MLflow

Kubeflow

PI System

Job description

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 in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics or Operations Research is strongly preferred.
• Equivalent demonstrated experience will be considered in place of a specific degree title, where the candidate can evidence a strong quantitative foundation and a substantial delivery record.
• Preferred certifications: cloud AI/ML (AWS Certified Machine Learning, Azure AI Engineer, Google Professional Machine Learning Engineer); project or programme management (PMP, PRINCE2 or a recognised agile certification); AI governance (ISO/IEC 42001 lead implementer or auditor, IAPP AIGP).

Experience

• Minimum 7 years of experience in Artificial Intelligence, Machine Learning, or Data Science, Machine Learning, or Data Science, of which at least 3 years leading technical teams or functions, including hiring, performance management and technical mentoring,
• Proven experience in successfully delivering end-to-end AI projects into production, into production, with at least two systems the candidate can describe in detail, including the measured business impact on cost, throughput, availability, recovery, quality or safety,
• Previous experience leading software development and technical teams,
• Experience in asset-intensive or process industries such as mining, metals, cement, energy, oil and gas, or heavy construction, with mining or minerals processing experience strongly preferred,
• Experience working with operational technology and industrial data environments, including sensor telemetry, historians and plant control systems,
• Experience delivering across multiple sites and countries, including remote operations with limited connectivity and infrastructure, and willingness to travel to operating sites in West Africa,
• Experience owning a budget, building business cases and presenting to and influencing executive stakeholders,
• Preferred: experience designing or running organisational capability-building, AI enablement or training programmes, or partnering with HR and Learning & Development on skills and competency frameworks,
• Preferred: experience operating within a group or holding structure serving multiple operating companies at differing levels of digital maturity,
• Experience managing large-scale enterprise projects, including project planning and resource management.

Technical Skills

• Strong expertise in Artificial Intelligence, Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) algorithms,
• Working command of large language model systems: prompt design, retrieval-augmented generation, the trade-offs between fine-tuning and prompting, agentic workflows and tool use, and the evaluation and guardrail methods that make such systems safe to deploy in an enterprise setting,
• Computer vision for industrial applications such as safety and PPE monitoring, equipment inspection, and material or ore characterisation (preferred),
• Solid knowledge of MLOps processes, cloud-based AI infrastructures (AWS, Azure, GCP), and data management, including practical MLOps tooling (MLflow, Kubeflow or cloud-native equivalents), containerisation (Docker, Kubernetes), and the ability to design hybrid cloud, on-premise and edge architectures for sites with limited or intermittent connectivity,
• Industrial and operational technology data integration: historians (for example PI System), SCADA and IoT telemetry, and edge inference (preferred, highly valued),
• Advanced proficiency in Python and the Machine Learning ecosystem, with strong SQL and relational database knowledge,
• Proven experience developing and deploying regression, classification, and time-series forecasting models from scratch into production environments,
• Strong understanding of data preprocessing, model validation, and performance evaluation metrics,
• Experience monitoring AI models in production environments and managing continuous model retraining,
• Familiarity with AI governance and risk frameworks, including EU AI Act risk classification, ISO/IEC 42001 and the NIST AI Risk Management Framework, together with working knowledge of data protection under KVKK and GDPR and of cross-border data transfer,
• Secure-by-design practice for AI systems: access control, data confidentiality, prompt injection and model misuse risks, and third-party model and vendor risk assessment,
• Strong analytical thinking, problem-solving, visionary leadership, and strategic decision-making capabilities, strategic decision-making capabilities, with the ability to translate between operational reality and technical possibility and to remain credible with mine and plant managers as well as with data scientists and the executive committee,
• Advanced written and spoken English, the working language across international operations.