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 [http://www.oliverwyman.com/], or
follow us on LinkedIn [https://www.linkedin.com/company/oliver-wyman]and X
[https://x.com/OliverWyman].

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