Senior AI Engineer Consultant

Tech for People Unlocks the Future At Devoteam, we believe that technology, combined with strong human values, can actively drive change for the better. Discover how Tech for People is shaping the future, creating a positive impact on individuals and the world around us. With over 25 years of passion for technology and a presence in 18+ countries across EMEA and beyond, we are committed to leveraging innovation, expertise, and human-centric values to make a difference. Devoteam Culture & Values: True innovation is born from a powerful culture, fused with meaningful values. Culture: Fair and courageous Everyone is treated fairly – this fuels bravery. At Devoteam, we always make fair decisions. We listen and are willing to be challenged, taking courageous decisions as a result. We help our employees to progress at every step and congratulate those who deserve it. Ambition and results Ambition is nurtured at every step – this drives results. We are ambitious entrepreneurs with a taste for performance, growth and celebrating success. Commitments are always kept as we seek to achieve profitable growth to create value and employment. We aim to bring as much value as possible to our clients, at every touchpoint. Learning and innovating Curiosity and learning are at our core – this stimulates innovation. At Devoteam, we are curious. We learn and embrace innovation constantly to meet challenges and build partnerships of excellence. Caring and sharing A caring attitude is infused into our culture – this encourages sharing. We believe in the power of teams, we promote support and collaboration. At Devoteam, we care about our teams and want to work in a positive, productive environment. We support the development of talent and careers, Values: • Respect • Frankness • Passion Job Description Key Responsibilities: • Build enterprise-grade LLM applications using techniques such as RAG, tool calling, structured generation, prompt engineering, and agentic workflows. • Implement and optimize LLM inference, including model serving, quantization, caching, batching, and GPU utilization where relevant. • Develop LLM evaluation frameworks covering accuracy, relevance, hallucination, safety, latency, and cost. • Work with open-source and commercial foundation models and assess their suitability for specific use cases. • Build reusable components for enterprise AI applications, including knowledge retrieval, model gateways, evaluation pipelines, and agent orchestration. Qualifications Required Qualifications & Skills: • Deploy AI/ML workloads to cloud, on-premise, and hybrid environments. • Work with Docker, Kubernetes, GPU infrastructure, and cloud AI/ML services. • Implement model versioning, experiment tracking, monitoring, logging, and automated deployment pipelines. • Optimize AI workloads for scalability, reliability, latency, and cost. • Troubleshoot production AI systems and continuously improve system performance Education: Bachelor's degree in computer science, Software Engineering, Computer Information Systems (CIS), AI and Data. Professional Experience • 7+ years of experience in AI Engineering. • Experience working with [Clients / Government / Consulting / GCC / Saudi market]. Soft Skills • Strong communication and presentation skills. • Strong stakeholder management and interpersonal skills. • Analytical and problem-solving capabilities. • Consulting and client-facing mindset. • Ability to work independently and collaboratively. • Strong organizational and time-management skills. Additional Information Language Requirements • Arabic Speakers • English Location & Employment Details • Location: Riyadh • Employment Type: Full-time. Show more Show less

Deploy AI/ML workloads to cloud, on-premise, and hybrid environments. Work with Docker, Kubernetes, GPU infrastructure, and cloud AI/ML services. Implement model versioning, experiment tracking, monitoring, logging, and automated deployment pipelines. Optimize AI workloads for scalability, reliability, latency, and cost. Troubleshoot production AI systems and continuously improve system performance

Build enterprise-grade LLM applications using techniques such as RAG, tool calling, structured generation, prompt engineering, and agentic workflows. Implement and optimize LLM inference, including model serving, quantization, caching, batching, and GPU utilization where relevant. Develop LLM evaluation frameworks covering accuracy, relevance, hallucination, safety, latency, and cost. Work with open-source and commercial foundation models and assess their suitability for specific use cases. Build reusable components for enterprise AI applications, including knowledge retrieval, model gateways, evaluation pipelines, and agent orchestration.