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AI Solution Architect; AWS + Azure
Job Description & How to Apply Below
We have an urgent requirement for an AI Solution Architect (AWS + Azure) for our banking client in Abu Dhabi, UAE.
- Strong experience on AWS:
Sage Maker, Lambda, EKS (Kubernetes) – Must - Strong experience on Azure:
Azure ML, Azure OpenAI, AI Foundry – Must - Strong experience on designing production‑grade AI/ML platforms for enterprise‑scale workloads – Must
- Strong experience on GenAI implementations (e.g., Azure OpenAI, LLMs) – Must
- Strong experience on RAG (Retrieval‑Augmented Generation) pipelines – Must
- Strong experience on implementing MLOps pipelines (CI/CD for models, monitoring, retraining) – Must
We are hiring an experienced AI Solution Architect to design and lead enterprise‑scale AI/ML and Generative AI platforms for the banking domain. This role requires strong hands‑on experience with AWS and Azure, deep understanding of secure cloud architectures, and the ability to lead engineering teams end to end.
You will work closely with business, data science, engineering, and security teams to build scalable, compliant, and production‑ready AI solutions.
Key Responsibilities- Design and architect enterprise‑grade AI/ML and GenAI platforms for banking use cases.
- Lead and mentor AI/ML and platform engineers.
- Build secure and scalable cloud architectures using AWS (Sage Maker, EKS, Lambda) and Azure (Azure ML, Azure OpenAI, AI Foundry).
- Design and implement MLOps pipelines (model training, deployment, monitoring, retraining).
- Implement governance, security, and compliance aligned with banking regulations.
- Design RAG (Retrieval‑Augmented Generation) architectures for GenAI applications.
- Work with Kubernetes for containerized AI workloads.
- Collaborate with stakeholders to translate business requirements into technical solutions.
- Ensure performance, reliability, and cost optimization of AI platforms.
- 8+ years of experience in AI/ML solution architecture.
- Strong experience in banking or financial services AI platforms.
- Hands‑on expertise with AWS Sage Maker and Azure ML.
- Experience with Azure OpenAI and GenAI use cases.
- Solid understanding of MLOps, model governance, and AI lifecycle management.
- Experience with Kubernetes (EKS) and cloud‑native architectures.
- Strong knowledge of security, data privacy, and regulatory requirements.
Skills:
azure, aws, architect, ai
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