Oliver Wyman - Digital - AI Product Owner - Dubai/Abu Dhabi

About the Role
As an AI Product Engineer, you will build the AI products and agentic solutions behind the company's AI-first transformation work, for our clients and for the firm. You will own how an AI product is built, secured, and made reliable—from data and retrieval, through agent orchestration and integration, to user interfaces, deployment, evaluation, and ongoing improvement.
You will work in small, multidisciplinary teams alongside AI Product Owners, consultants, industry experts, and client teams, building on live engagements. The role suits engineers who want to ship production-grade AI, see its business impact first-hand, and develop the breadth to lead complete AI products.

What You Will Do

• Develop and integrate user interfaces, backend services, and APIs into complete AI applications, applying appropriate frontend frameworks and user experience principles.
• Build single and multi-step agents with tools, state, memory, and human checkpoints. Design, test, and optimize agent instructions, context, memory, and tool specifications to improve task completion, reliability, latency, and cost.
• Design agent workflows with durable state, resumability, failure recovery, safe retries, timeouts, and rollback or compensation for actions that cannot simply be reversed.
• Build retrieval and knowledge pipelines, prepare structured and unstructured data, and design knowledge stores with appropriate data quality, lineage, protection, and access controls.
• Design, build and deploy AI applications on cloud platforms such as AWS, including compute, storage, databases, networking, and containerized services. Integrate with enterprise systems through APIs and standard integration patterns, supporting client cloud and on-premise environments.
• Apply secure engineering and least-privilege access, and design permission boundaries, handling of untrusted content, connector security, and isolation between clients or tenants. Support IT, Risk, and Legal approvals.
• Build evaluation and regression-testing practices using representative tasks, execution traces, and production feedback. Monitor quality, latency, and cost; design for maintainable operations and work with client technology or platform teams to support products in production.
• Translate product objectives into sound technical decisions with AI Product Owners, explain trade-offs and risks to non-technical stakeholders, and prepare client teams to run what we build.
• Use AI coding tools productively while owning the quality of everything you ship, contribute reusable components and standards, and, as your experience grows, lead workstreams and mentor others.

Skills And Qualifications
We hire across experience levels and assess what candidates can demonstrate. For early-career hires, the priority is strong coding fundamentals, hands-on AI application experience, and sound technical judgement. Complex architecture, production operations, and team leadership can develop with experience. We look for most of the following:

• A degree in computer science, engineering, or a related field, or equivalent practical experience.
• Demonstrated software engineering experience through employment, independent projects, coursework, or open-source contributions; production delivery experience is valued for more senior candidates.
• Strong coding skills in Python and/or TypeScript, including readable code, debugging, source control, and automated testing; exposure to code review and CI/CD is an advantage.
• Evidence of building a working LLM-powered application or agent, using tools or APIs, retrieval, or prompt and context design, and being able to explain what worked and what failed.
• Ability to develop software components and integrate with APIs, with a willingness to grow across user interfaces, backend services, and full-stack delivery.
• Foundational data skills, including SQL and working with structured or unstructured information; deeper pipeline and knowledge engineering can develop in the role.
• Hands-on experience with cloud platforms, preferably AWS, including deploying applications, managing cloud services and working with containers, APIs, and cloud infrastructure.
• Understanding of cloud security, identity and access management, networking, monitoring, and cost optimization.
• A security-first mindset and awareness of AI-specific risks, such as prompt injection, unsafe tool use, secrets exposure, and data leakage.
• Understanding of software testing and why agent outputs require scenario-based evaluation, including both expected and failed behaviours.
• Product curiosity and the ability to explain technical decisions and trade-offs to technical and non-technical colleagues.
• Excellent written and verbal communication in English.

Nice To Have

• Experience with multi-agent orchestration, complex enterprise integrations, vector or graph databases, or observability tooling.
• Experience with AWS services such as Bedrock, Lambda, ECS/EKS, S3, RDS, IAM, and CloudWatch, or equivalent services on Azure or Google Cloud.
• Experience with infrastructure as code (e.g., AWS CDK), automated cloud deployments, and enterprise cloud security practices.
• Familiarity with AI security and governance frameworks.
• Experience in consulting, client-facing, startup, or regulated and government environments.
• A portfolio of code, working demos, or shipped products that illustrates individual contributions.

Why Work Here?
Working as part of our global, entrepreneurial company, you will do meaningful work from day one. We are looking for individuals who challenge the norm and constantly strive to build something new for the firm and the world around us.
We have a collaborative, supportive and impactful team environment—we want you to bring your authentic self and enjoy working alongside diverse and down-to-earth colleagues who do serious work but don’t take themselves too seriously.