Location: Dubai, UAE – Onsite
Experience Required: 4–8+ Years in Artificial Intelligence, Machine Learning, or AI Engineering
Industry: Technology / Consumer Applications / On-Demand Services / Digital Marketplaces
Key Responsibilities
- Design, develop, deploy, and maintain Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), and Generative AI solutions for a fast-growing consumer technology and on-demand services platform.
- Build and integrate LLM-powered applications, intelligent automation, AI assistants, conversational AI, and recommendation systems to enhance customer experience, operational efficiency, and platform capabilities.
- Develop and implement Applied AI solutions that address real-world business challenges across consumer applications, digital marketplaces, service delivery, and customer engagement.
- Design, develop, and optimize Machine Learning models for predictive analytics, customer behavior analysis, personalization, intelligent recommendations, and operational decision-making.
- Build and maintain scalable MLOps pipelines for model training, evaluation, deployment, monitoring, versioning, and continuous improvement.
- Develop and deploy Retrieval-Augmented Generation (RAG) pipelines, semantic search capabilities, vector databases, and knowledge retrieval systems for LLM-powered applications.
- Integrate LLM APIs and AI services, including OpenAI, Anthropic Claude, Google Gemini, or equivalent models, into production applications and backend services.
- Implement Prompt Engineering, LLM Fine-Tuning, Model Evaluation, and AI Optimization techniques to improve model accuracy, reliability, performance, and cost efficiency.
- Develop AI-powered features for mobile applications, web platforms, digital marketplaces, and on-demand consumer services.
- Collaborate with Backend Engineers, Mobile Engineers, Product Managers, and Founders to identify AI opportunities, define technical requirements, and deliver production-ready AI capabilities.
- Build and maintain Python-based AI services, REST APIs, microservices, and backend integrations to support AI-powered product features.
- Design and implement AI Agent workflows, intelligent automation, tool-calling systems, and multi-step AI orchestration where appropriate.
- Utilize AI development frameworks such as LangChain, LangGraph, LlamaIndex, Hugging Face Transformers, or similar technologies to accelerate AI application development.
- Work with Vector Databases, including Pinecone, Weaviate, FAISS, ChromaDB, or equivalent solutions, for semantic search and retrieval applications.
- Implement scalable AI infrastructure using AWS, Microsoft Azure, Google Cloud Platform (GCP), or similar cloud environments.
- Develop and maintain CI/CD pipelines, Docker containers, Kubernetes deployments, and automated ML workflows to support reliable AI model deployment.
- Monitor AI model performance, latency, accuracy, hallucinations, data quality, inference costs, and production reliability.
- Conduct AI model testing, performance benchmarking, experimentation, and continuous optimization to ensure high-quality production AI systems.
- Support AI-driven personalization, intelligent search, service matching, demand forecasting, customer support automation, and operational optimization where relevant to the platform.
- Ensure AI solutions follow appropriate Data Privacy, Security, Responsible AI, and Model Governance practices.
- Troubleshoot production AI systems, resolve model performance issues, and optimize inference pipelines for scalability and efficiency.
- Stay updated with emerging developments in Generative AI, LLMs, Agentic AI, Applied Machine Learning, and MLOps, identifying opportunities to introduce practical AI capabilities into the product.
- Contribute to AI architecture decisions, technical documentation, engineering best practices, and the long-term AI development roadmap.
Experience & Professional Qualifications
- 4–8+ years of relevant experience in Artificial Intelligence, Machine Learning Engineering, Applied AI, Data Science, or AI Software Development, with demonstrated experience delivering production AI solutions.
- Strong hands-on experience with Large Language Models (LLMs), Generative AI, MLOps, and Applied AI.
- Proven ability to design, develop, integrate, and deploy AI/ML models and LLM-powered applications into real-world production environments.
- Advanced programming skills in Python, with practical experience developing AI applications, machine learning pipelines, backend services, and API integrations.
- Strong understanding of Machine Learning, Deep Learning, Natural Language Processing (NLP), Generative AI, and Transformer-based architectures.
- Hands-on experience with LLM frameworks and APIs, including OpenAI, Anthropic Claude, Google Gemini, Hugging Face, or similar technologies.
- Practical experience with Prompt Engineering, Retrieval-Augmented Generation (RAG), Embeddings, Vector Search, and LLM Evaluation.
- Experience developing applications using LangChain, LangGraph, LlamaIndex, or equivalent LLM orchestration frameworks.
- Strong knowledge of MLOps principles and practices, including model deployment, experiment tracking, model versioning, monitoring, retraining, and lifecycle management.
- Experience with MLflow, Kubeflow, AWS SageMaker, Azure Machine Learning, Vertex AI, or equivalent MLOps platforms.
- Proficiency with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, or similar technologies.
- Experience designing and deploying scalable, production-grade AI systems with appropriate performance, reliability, and cost optimization.
- Strong experience building and integrating REST APIs, FastAPI, Flask, or equivalent Python-based backend frameworks.
- Familiarity with Docker, Kubernetes, CI/CD pipelines, Git, and cloud-based deployment environments.
- Experience working with AWS, Microsoft Azure, or Google Cloud Platform, particularly for AI/ML infrastructure and application deployment.
- Knowledge of SQL, NoSQL databases, data pipelines, data processing, and database integration for AI-powered applications.
- Familiarity with Vector Databases, including Pinecone, Weaviate, FAISS, ChromaDB, or equivalent solutions.
- Experience with AI Agents, Agentic AI, Multi-Agent Systems, and Intelligent Workflow Automation is highly advantageous.
- Understanding of LLM Fine-Tuning, LoRA, Quantization, Model Optimization, and Inference Performance is advantageous.
- Experience developing recommendation engines, personalization models, predictive analytics, intelligent search, or customer behavior models is highly desirable.
- Experience working with consumer-facing mobile applications, digital marketplaces, on-demand service platforms, or super apps is strongly preferred.
- Candidates with previous experience at Careem, CAFU, Talabat, Deliveroo, Noon, Uber, or similar consumer technology, marketplace, mobility, or on-demand platforms are highly desirable.
- Experience building AI capabilities for service marketplaces, ride-hailing, delivery platforms, e-commerce, logistics, or consumer technology applications is advantageous.
- Demonstrated ability to translate business requirements and product challenges into practical, scalable, and commercially valuable AI solutions.
- Experience collaborating closely with Product Managers, Software Engineers, Backend Developers, Mobile Engineers, and cross-functional product teams.
- Strong understanding of AI Model Evaluation, Hallucination Detection, Model Monitoring, Responsible AI, Data Privacy, and AI Security.
- Ability to work in a startup, scale-up, or fast-paced product engineering environment, taking ownership of AI solutions from initial concept through production deployment.
- Strong analytical thinking, problem-solving, technical troubleshooting, communication, and stakeholder collaboration skills.
- Ability to work independently, manage competing priorities, and deliver AI features in an agile, product-driven engineering environment.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical discipline is preferred.
- Relevant certifications in AWS Machine Learning, Azure AI Engineer, Google Professional Machine Learning Engineer, or equivalent AI/ML technologies are advantageous.
