Oliver Wyman

Описание: Company: Oliver Wyman Description: Oliver Wyman Digital is a dedicated practice within Oliver Wyman, we deliver world-class products that give our clients a competitive edge and our mission is to leave a positive impact on the world. We have mixed agency skills with our consulting roots in order that we can best answer some of the largest companies' toughest questions. Our global dynamic team deliver at the highest levels across many specialist skill sets including Design, Data Science & Analytics, Project Delivery and Engineering. We're committed to growth that provides our people the most exciting and fulfilling opportunities and we are looking for more people to join us. Задачи: As an AI Product Engineer, you will build the AI products and agentic solutions behind Oliver Wyman's AI-first transformation work, for our clients and for the firm. You 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. 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. Требования: 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 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,