About the job
General Description
Join a major AI and digital transformation initiative with a leading banking organization in the UAE, supporting the development and deployment of advanced AI, Machine Learning, Generative AI, LLM, and intelligent automation solutions across enterprise and digital banking environments.
We are looking for an experienced AI Engineer / Generative AI Engineer with strong hands-on expertise across Machine Learning, Deep Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Multimodal AI, and production-grade AI engineering.This is a highly technical and hands-on engineering position. The successful candidate will be responsible for designing, developing, integrating, and deploying scalable AI solutions and platforms, combining advanced AI/ML capabilities with strong software engineering, cloud, data, and MLOps practices.
The role requires someone who can work across the complete AI engineering lifecycle — from data preparation, model development and LLM integration through RAG architecture, vector search, AI agents, APIs, deployment, monitoring, and production optimization.The ideal candidate will have strong Python engineering capabilities, practical experience working with modern LLMs and transformer-based models, and the ability to deploy enterprise-grade AI applications using Azure and/or AWS, Docker, Kubernetes, microservices, APIs, and MLOps frameworks.
Experience within banking, financial services, FinTech, or highly regulated enterprise environments would be highly advantageous.
- Key ResponsibilitiesDesign, develop, and deploy production-grade AI, Machine Learning, Deep Learning, and Generative AI solutions
- Build enterprise applications and services powered by Large Language Models (LLMs) and transformer-based architectures
- Design and implement Retrieval-Augmented Generation (RAG) solutions combining LLMs with enterprise and domain-specific data
- Develop and optimize embedding, semantic search, retrieval, chunking, reranking, and grounding pipelines
- Integrate and manage vector databases and vector search technologies, including Pinecone, FAISS, pgvector, Chroma, Weaviate, Milvus, Azure AI Search, or equivalent technologies
- Design and develop Agentic AI and autonomous AI workflows, including single-agent and multi-agent architectures
- Work with AI orchestration frameworks and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or equivalent frameworks
- Develop multimodal AI solutions combining text, vision, image, speech, audio, and other data modalities where required
- Develop and integrate NLP / NLU capabilities for enterprise AI use cases
- Support development of personalization, recommendation, ranking, and intelligent decisioning systems
- Design and integrate knowledge graphs, GraphRAG, semantic knowledge layers, and structured/unstructured enterprise knowledge sources
- Build reusable and scalable AI services and microservices using Python and modern application-development frameworks
- Develop and integrate REST APIs, FastAPI, GraphQL, and other service interfaces for AI applications
- Deploy and operate AI/ML workloads across Azure and AWS cloud environments
- Containerize AI applications and services using Docker and Kubernetes
- Implement robust MLOps / LLMOps practices covering model deployment, versioning, registries, monitoring, observability, experimentation, and lifecycle management
- Work with technologies such as MLflow, CI/CD pipelines, model registries, monitoring platforms, and automated deployment frameworks
- Design scalable architectures capable of supporting high-volume and enterprise-grade AI workloads
- Work with SQL and NoSQL databases, including PostgreSQL, MongoDB, Cassandra, and similar technologies
- Integrate AI applications with data lakes, enterprise data platforms, APIs, operational systems, and structured/unstructured datasets
- Optimize AI applications for performance, scalability, reliability, latency, cost, and production readiness
- Implement appropriate controls around AI security, data privacy, access management, responsible AI, and model governance
- Collaborate closely with Data Engineers, Data Scientists, ML Engineers, Software Engineers, Solution Architects, Product Teams, Security Teams, and business stakeholders
- Translate business requirements and complex use cases into practical, scalable, and production-ready AI solutions
- Evaluate emerging LLMs, foundation models, AI frameworks, vector technologies, agentic architectures, and Generative AI tools
- Support technical documentation, architecture design, solution reviews, testing, troubleshooting, and continuous improvement of AI platforms.
- Required ExperienceStrong professional experience in AI Engineering, Machine Learning Engineering, Applied AI, Generative AI, or related technical roles
- Advanced hands-on programming expertise in Python, particularly for AI/ML engineering and production AI applications
- Strong understanding of Machine Learning and Deep Learning concepts, architectures, and development practices
- Hands-on experience with Large Language Models (LLMs) and modern Generative AI technologies
- Strong understanding of transformer-based architectures and models
- Proven experience building and deploying RAG solutions
- Hands-on experience with embeddings, semantic search, vector retrieval, chunking strategies, and retrieval optimization
- Experience with vector databases such as Pinecone, FAISS, pgvector, Chroma, Weaviate, Milvus, or equivalent
- Experience building or integrating Agentic AI systems and AI orchestration frameworks
- Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or similar technologies
- Experience working with multimodal AI, including combinations of text, vision, image, speech, and/or audio
- Strong knowledge of NLP / NLU and related AI techniques
- Understanding of knowledge graphs and knowledge-based AI architectures
- Experience building scalable AI platforms, services, APIs, and microservice-based architectures
- Strong hands-on experience with Azure and/or AWS cloud platforms
- Strong understanding of Docker, Kubernetes, CI/CD, and cloud-native deployment
- Experience with MLOps / LLMOps, including model deployment, monitoring, versioning, experimentation, and lifecycle management
- Experience with MLflow or comparable ML lifecycle platforms
- Strong knowledge of SQL and NoSQL technologies, including platforms such as PostgreSQL, MongoDB, Cassandra, or equivalent
- Experience developing and consuming REST APIs, FastAPI, GraphQL, or equivalent API technologies
- Experience working with data lakes, structured and unstructured datasets, and enterprise data platforms
- Strong software engineering fundamentals and ability to develop maintainable, reusable, scalable, and production-quality code
- Strong problem-solving skills and ability to translate complex requirements into practical AI engineering solutions.
- Preferred ExperienceExperience building enterprise-scale Generative AI and LLM platforms
- Strong hands-on experience with Agentic AI / multi-agent systems
- Experience implementing GraphRAG, knowledge graphs, or graph-based retrieval architectures
- Experience developing recommendation, personalization, ranking, or intelligent decisioning systems
- Strong experience with multimodal foundation models
- Experience with major commercial and open-source model ecosystems such as OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Hugging Face, or equivalent
- Experience with model fine-tuning, evaluation, prompt engineering, guardrails, and LLM optimization
- Knowledge of AI observability, model monitoring, evaluation frameworks, and production LLM performance management
- Understanding of Responsible AI, AI governance, model risk, data privacy, and AI security
- Experience working within banking, financial services, FinTech, or another highly regulated enterprise environment
- Experience integrating AI capabilities into complex enterprise applications and existing technology ecosystems
- Exposure to data visualization and analytics platforms, including Tableau, Power BI, Superset, or equivalent
- Ability to collaborate with both highly technical engineering teams and business/product stakeholders.
- Work SetupLocation – UAE
- Leading Banking Organization
- AI / ML / Generative AI Engineering
- Hands-on Technical Engineering Role
- LLM / RAG / Agentic AI / Multimodal AI
- Cloud-Native AI Platform Development
- Contract / Freelance Engagement
- Immediate / ASAP Hiring Timeline
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