Company:
Oliver Wyman

Description:

About Oliver Wyman

Oliver Wyman, a Marsh (NYSE: MRSH) business, is a management consulting firm driven by deep industry insight, bold innovation, and a collaborative approach that cuts through complexity to help organizations navigate their most defining transformative moments.

For more information, visit oliverwyman.com, or follow us on LinkedIn and X.

Position Summary:

We are seeking an ML Engineer – Generative AI & Unstructured Data to design, build, and operationalize AI systems that transform enterprise unstructured data into scalable, production-ready services and intelligent applications.
This is a role for an engineer who can work autonomously on clearly scoped components, contribute to design discussions, and grow toward broader technical ownership over time. You are not expected to lead architecture decisions independently yet, but you should be a confident, self-directed practitioner.
The role sits at the intersection of machine learning engineering, MLOps, and Generative AI. You will build robust ML pipelines,



deploy and monitor AI services, and develop scalable APIs and retrieval systems that enable internal applications and business teams to consume AI capabilities securely and reliably.
You will work closely with Data Scientists, Data Engineers, and Application Developers to produce models and deliver enterprise-grade AI solutions using modern cloud and ML tooling.

Key Responsibilities:
- Design and build scalable ML pipelines that ingest and process unstructured data including documents, transcripts, images, logs, and free-form text.
- Develop and maintain production-ready AI services and APIs consumed by internal applications and analytics platforms.
- Productionize machine learning and Generative AI solutions, including model packaging, containerization, deployment, versioning, and lifecycle management.
- Build and integrate Generative AI workflows such as Retrieval-Augmented Generation (RAG), embedding

📌 Oliver Wyman (Ciudad de México)
🏢 Marsh Mclennan
📍 Ciudad de México