Key ResponsibilitiesAI & ML Solution Development & Integration
Design, develop, and implement AI solutions, including generative AI, machine learning models, neural networks, and optimization algorithms, to improve business process efficiency and effectiveness.
Select the appropriate modelling approach for each problem and articulate the trade-offs behind that choice.
Own the machine learning lifecycle: dataset construction, metrics definition, acceptance criteria in accordance with the stakeholders needs, evaluation strategies.
Translate business and operational needs into scalable AI-enabled tools, applications, and workflows.
Support the deployment and integration of AI/ML models into existing business and technical systems, software environments, and products where applicable, including monitoring for drift, performance degradation, and running cost.
Technical Implementation & Architecture
Collaborate with domain experts to identify high-value use cases and define technical requirements for AI solutions.
Define and document the solution architecture end to end: from data sources to the integration with the existing enterprise and technical IT landscape.
Design cloud-ready and on-premises solutions aligned with company IT, cybersecurity and data-governance standards.
Integrate AI/ML capabilities into hardware, software, and business process ecosystems in a way that supports reliability, usability, and maintainability.
Contribute to the development of robust, production-ready AI solutions suitable for industrial environments.
Write clear technical specifications, statements of work, and acceptance criteria for work delivered by external contractors, software vendors, or internal digital teams.
Contribute to supplier, platform, and tool selection through structured technical evaluation, benchmarking, and proof-of-concept comparison.
Steer and review the work of internal & external partners: technical follow-up, design reviews, code and model reviews, quality gates, and acceptance testing; remaining the technical owner and guardian of the delivered solution.
Ensure solutions remain maintainable after handover through documentation, knowledge transfer, and clearly assigned ownership, so that delivered tools do not become orphaned.
Data Strategy & Analytics
Lead or support the collection, processing, structuring, and analysis of large-scale operational and business data.
Assess data readiness ahead of any development (availability, quality, labelling needs, access rights, confidentiality) and define strategies to close the gaps.
Identify patterns, trends, and performance improvement opportunities using advanced analytics and AI methods.
Develop data-driven solutions such as predictive maintenance, anomaly detection, quality and performance prediction, forecasting, cost analysis, document and requirement analysis, and knowledge support tools.
Cross-Functional Collaboration
Work closely with technical, operational, business and IT teams to ensure AI solutions meet business and industry requirements for safety, reliability, performance, and scalability.
Communicate technical concepts clearly to both technical and non-technical stakeholders.
Help align AI initiatives with business priorities, operational goals, and constraints.
Support end-user adoption: training, onboarding, feedback loops and measurement of the benefits realized once the solution is live.
Education
Bachelor/Master’s degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field.
Experience
Several years (2 to 4) of professional experience in artificial intelligence, machine learning, data science, software engineering, or a comparable technical role.
