AI Product Engineer - Dubai & Abu Dhabi (Full-Stack AI)

Oliver Wyman

Dubai

On-site

AED 300,000 - 480,000

Full time

4 days ago

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Job summary

Oliver Wyman Digital seeks an AI Product Engineer to build AI-powered products and agentic solutions behind the firm’s AI-first transformation. You’ll own the lifecycle from data to deployment, ensuring secure, reliable, and scalable AI applications.

You will collaborate with multidisciplinary teams and client stakeholders to ship production-grade AI that delivers business impact. You will work on end-to-end architecture, agent orchestration, data pipelines, and cloud deployments, applying best

Qualifications

Degree or equivalent practical experience in computer science, engineering, or related field.
Software engineering experience with production delivery is valued especially for senior candidates.
Strong coding skills in Python and/or TypeScript, incl. readable code, debugging, and CI/CD exposure.
Experience building working LLM-powered applications or agents using tools or APIs; able to explain outcomes.
Ability to develop software components and integrate with APIs; willingness to grow across UI, backend, and full-stack.
Foundational data skills including SQL; familiarity with structured and unstructured data.
Hands-on experience with AWS and containerized services; cloud deployments and APIs.
Understanding of cloud security, IAM, networking, monitoring and cost optimization.
Security-first mindset for AI-specific risks (prompt injection, data leakage, etc.).
Understanding of software testing and evaluation for agent outputs.
Product curiosity and ability to explain technical decisions to diverse stakeholders.

Responsibilities

Full-stack AI product development: build UI, backend services, and APIs for complete AI apps.
Agent development and context engineering: design, test, and optimize agent instructions, memory, and tools.
Reliable agent execution: design durable state, retries, timeouts, and safe rollback mechanisms.
Data and knowledge engineering: build retrieval pipelines and knowledge stores with quality controls.
Architecture and integration: deploy AI apps on cloud platforms (AWS) with containerized services.
Security and AI safety: enforce least privilege and secure integration boundaries.
Evaluation and operations: monitor quality, latency, and cost; iterate in production.
Collaboration and client impact: translate product goals into technical decisions for clients.
Continuous improvement: contribute reusable components and mentor others.