AI Engineer

• Analyse existing business processes and workflows to identify operational inefficiencies
• Recommend automation opportunities and AI-driven improvements
• Collaborate with stakeholders across departments to understand business challenges and requirements
• Document current and future-state processes
• Develop digital transformation roadmaps aligned with business objectives
• Identify high-impact AI use cases across business functions
• Evaluate emerging AI technologies and recommend suitable solutions
• Support strategic planning for AI implementation
• Design, develop, and deploy AI-powered solutions
• Collaborate with software engineering, product, and data teams for end-to-end implementation
• Build scalable AI workflows and automation pipelines
• Ensure solutions meet business, security, and performance requirements
• Integrate AI tools including ChatGPT, Claude, Gemini, OpenAI APIs, and Automation platforms (Zapier, UiPath, Make, Microsoft Power Automate)
• Develop prompt engineering frameworks and reusable AI workflows
• Improve operational efficiency through intelligent automation
• Lead AI adoption initiatives across business units
• Conduct workshops and user training sessions
• Promote AI awareness and innovation throughout the organization
• Support teams during digital transformation initiatives
• Define KPIs for AI initiatives
• Measure business impact and ROI
• Monitor solution performance and recommend optimizations
• Prepare dashboards and executive reports for leadership
• Stay updated on emerging AI technologies and industry trends
• Maintain internal AI documentation and best practices
• Support cross-functional teams with AI expertise
• Contribute to long-term digital transformation strategies

AI Research Scientist

• Conduct end-to-end research and engineering on vision-language models, covering training, evaluation, and optimization across the full model development lifecycle.
• Design and implement post-training pipelines including supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback.
• Develop and maintain high-quality multimodal datasets, including data curation, filtering, and balancing for domain-specific tasks.
• Drive model efficiency and deployability, adapting models for resource-constrained environments using compression and optimization techniques.
• Design and implement evaluation frameworks and benchmarks to measure model performance, robustness, and real-world task success.
• Build and scale training workflows across distributed GPU infrastructure.
• Identify and resolve bottlenecks in training pipelines to achieve state-of-the-art model quality on target benchmarks.
• Contribute to and leverage open-source ecosystems including models, datasets, and tooling to accelerate development.
• Stay current with the latest research in multimodal learning and vision-language systems, translating relevant findings into practical improvements.
• Publish research findings in top-tier AI conferences and journals where applicable.

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AI Engineer

AI Research Engineer – Multi-Modal, Vision