Staff AI Engineer

Job Description Summary

Job Description

AI & 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.

Solution Delivery, Partner & Contractor Management

• 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.

Continuous Innovation

• Evaluate emerging technologies such as edge AI, synthetic data, reinforcement learning, and large language models for industrial and business applicability.
• Stay current with developments in AI, machine learning, and digital tools, and recommend practical adoption opportunities.
• Maintain an active technology watch on the AI tooling landscape and filter it, distinguishing capability gains from hype before proposing adoption.
• Contribute to building an innovation-oriented culture through knowledge sharing, experimentation, and continuous improvement.

Your Profile

Education

• Master’s degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related field.

Experience

• Overall 8+ Years Experience.
• Several years (2 to 4) of professional experience in artificial intelligence, machine learning, data science, software engineering, or a comparable technical role.
• Experience developing and deploying AI/ML solutions in industrial, technical, or other complex operational environments is preferred.

Technical Expertise

• Strong programming skills in Python, C++, or similar languages, and proficiency with modern development environments such as VS Code.

Additional Information

Relocation Assistance Provided:Yes