This position is at Marsh McLennan

The selection process will be entirely managed by Marsh McLennan.

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Company: Oliver Wyman

Description:

Who we are

At Oliver Wyman, a Marsh (NYSE: MRSH) business, we bring deep industry insight, bold innovation, and a collaborative approach that cuts through complexity to help organizations navigate their most defining transformative moments. DNA is Oliver Wyman's home for data, analytics, AI and engineering practitioners. We sit alongside our consulting colleagues and inside our clients' teams, turning real business problems into working technology. This is a hybrid role, with at least three days per week spent in the office or onsite with clients.

The opportunity

Forward Deployed Engineers take AI solutions from idea to secure production inside live client environments. You will embed with a client team and work like a member of their staff: understanding how the work actually gets done, translating messy real-world workflows into something an AI system can support, then building, testing and iterating alongside the people who will use it. As a senior member of the team you will do this without a defined brief. You will decide what is worth building, set the technical direction, and stand up the foundations the solution needs — integrations, permissions, hosting, data access — in a way that holds up in an architecture review and a security assessment. You will prove value in production at small scale rather than in a polished demo, and build the pipeline of the next use cases from what you learn on the ground. You will also be one of the people others learn from: shaping how we deliver, raising the technical bar,



and giving less experienced engineers the context and confidence to own their own work. The role rewards range. We look for people who are genuinely deep in one discipline and credible across the others: client delivery, AI and agentic systems, engineering and infrastructure, and the subject matter of the business they are working in. Our culture

- We are looking for exceptional talent with excellent communication skills
- Collaboration is key, both internally and with our clients. We believe we do our best work when we are together and working hand in hand with business users
- Curiosity is something we embrace and value highly
- We want people who are positive and passionate, have proven problem solving capabilities, can work quickly to find solutions to complex challenges and unlock big opportunities
- People need to be able to take ownership and be trusted to deliver, going the extra mile
- We want people who are highly motivated and have a high desire to learn

Responsibilities

- Embed within client teams to understand how work is really done, and frame business problems and commercial opportunities as AI problems — applying judgement to identify the true need beyond the stated requirement
- Lead delivery end to end: design, build, test, deploy and iterate AI solutions in live production environments




- Set the technical direction on your engagements, making the architectural calls and defending them to client engineering and security teams
- Stand up the technical foundations that solutions depend on, working with client IT to secure the right integrations, access and hosting
- Prove value in production at small scale, then work with colleagues to productise and scale what works
- Build a pipeline of new use cases from what you observe on the ground, and help clients build their own capability rather than a dependency on us
- Bring on other engineers as the work grows, and raise the standard of delivery around you through review, pairing and mentoring
- Challenge current systems and processes to achieve common goals through technical excellence and innovation
- Keep abreast of progress in AI and technology trends that keep us at the forefront of what is possible

What makes a great candidate

- Excellent programming and system design skills, with recent hands-on experience in Python and ideally one of C#/.NET, Java, Rust or C++
- A track record of taking AI or machine learning systems into production — not just prototypes — including substantial experience with LLMs, prompt design, agentic workflows, evaluation harnesses and retrieval pipelines
- Depth in the surrounding engineering: APIs and integrations, SQL, cloud platforms (Azure or AWS), version control, CI/CD, and the security and access considerations that come with deploying inside someone else's environment
- Proven ability to own a project from start to finish in an ambiguous setting, and the judgement to know what to build, what to b

📌 Oliver Wyman (Madrid)
🏢 Marsh Mclennan
📍 Madrid