AI Product Engineer: End-to-End AI Applications

Oliver Wyman; Dubai; On-site; AED 350,000 - 600,000; Full time

Job summary

Oliver Wyman Digital is seeking an AI Product Engineer to build AI products and agentic solutions for client engagements and internal transformations. You will own end-to-end development, from data and retrieval to orchestration, interfaces, deployment, evaluation and ongoing improvement.

You will collaborate with AI Product Owners, consultants and clients in small teams to ship production-grade AI and lead complete AI products as you grow.

Qualifications

• 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 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.

Responsibilities

• Full-stack AI products: Develop and integrate user interfaces, backend services, and APIs into complete AI applications, applying appropriate frontend frameworks and user experience principles.
• Agent development and context engineering: 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.
• Reliable agent execution: Design agent workflows with durable state, resumability, failure recovery, safe retries, timeouts, and rollback or compensation for actions that cannot simply be reversed.
• Data and knowledge engineering: Build retrieval and knowledge pipelines, prepare structured and unstructured data, and design knowledge stores with appropriate data quality, lineage, protection, and access controls.
• Architecture and integration: 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.
• Security and AI safety: 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.
• Evaluation and operations: 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.
• Collaboration and client impact: 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.
• Continuous improvement: 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

Python/TypeScript

LLM/agent experience

AWS

CI/CD

API integration

SQL/Data

Education

Degree in CS/Engineering or related field

Tools

AWS Bedrock/Lambda

Docker

Kubernetes

CDK/CI tooling

Job description

Oliver Wyman Digital is seeking an AI Product Engineer to build AI products and agentic solutions for client engagements and internal transformations. You will own end-to-end development, from data and retrieval to orchestration, interfaces, deployment, evaluation and ongoing improvement.

You will collaborate with AI Product Owners, consultants and clients in small teams to ship production-grade AI and lead complete AI products as you grow.