Oliver Wyman - ML Engineer - Generative AI & Unstructured Data - Mexico City

Oliver Wyman; Mexico; Hybrid; PHP 7,500,000 - 10,625,000; Full time

Job summary

Oliver Wyman is seeking an ML Engineer focused on Generative AI and unstructured data to design, build, and operationalize AI systems that convert unstructured data into scalable services.

The role blends ML engineering with MLOps, delivering robust pipelines, AI services, and retrieval workflows that empower internal users with secure, reliable AI capabilities.

Qualifications

• 3+ years of professional experience in ML Engineering, Applied AI, Data Science, or related roles.
• Experience building NLP pipelines, embeddings, or semantic search.
• Proficiency in Python and SQL; experience with PySpark and Databricks.
• Experience deploying production APIs for ML model serving (FastAPI or similar).
• Familiarity with MLOps including CI/CD, Docker, and Git workflows.
• Experience with cloud-based AI/ML platforms.
• Strong software engineering fundamentals.

Responsibilities

• Design and build scalable ML pipelines that ingest unstructured data (documents, transcripts, images, logs, text).
• Develop production-ready AI services and APIs for internal apps and analytics.
• Productionize ML/Generative AI solutions: packaging, deployment, versioning, lifecycle management.
• Build Generative AI workflows: Retrieval-Augmented Generation, embeddings, semantic search, LLM APIs.
• Implement monitoring and model performance tracking for reliability and SLA compliance.
• Collaborate with Data Scientists to move experiments to production.
• Contribute to CI/CD and MLOps: testing, deployment automation, retraining workflows.
• Translate business needs into scalable AI solutions.
• Document systems and decisions for team clarity.

Skills

Python

SQL

ML Ops

Docker

Tools

PySpark

Databricks

FastAPI

LangChain

Job 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, visitoliverwyman.com, or follow us onLinkedInandX.

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 pipelines, semantic search, and LLM-powered APIs.
• Implement monitoring, observability, and model performance tracking to ensure reliability, scalability, and SLA compliance of deployed systems.
• Collaborate with Data Scientists to transition experimental models into stable production environments.
• Contribute to CI/CD and MLOps practices including automated testing, deployment automation, retraining workflows, and infrastructure standardization.
• Partner with cross-functional stakeholders to translate business needs into scalable AI-enabled solutions.
• Document systems and decisions clearly so teammates can understand, extend, and maintain your work.

Experience Required:

• 3+ years of professional experience in ML Engineering, Applied AI, Data Science, or related engineering roles with a clear focus on production systems.
• Hands-on experience building solutions using unstructured data, including NLP pipelines, document processing, OCR, text classification, embeddings, entity extraction, or semantic search.
• Proficiency in Python and SQL; experience building scalable data and ML workflows using PySpark and Databricks or similar distributed compute platforms.
• Experience developing and deploying production APIs for ML model serving using FastAPI or similar frameworks.
• Familiarity with MLOps practices including CI/CD for ML, model monitoring, automated retraining, Docker, and Git-based workflows. Deep expertise is not required at this level, but you should be a competent practitioner.
• Experience working with cloud-based AI/ML platforms and enterprise deployment environments.
• Strong software engineering fundamentals, including readable code, testing, debugging, and performance optimization.

Preferred (Good To Have)

• Experience with Generative AI and LLM ecosystems including OpenAI, Azure OpenAI, or open-source models.
• Familiarity with orchestration frameworks such as LangChain or LlamaIndex.
• Experience working with vector databases and retrieval platforms such as Azure AI Search, Pinecone, or pgvector.
• Exposure to responsible AI practices including explainability, bias detection, governance, and model risk management.

Skills and Attributes:

• Engineering judgment: able to balance scalability, maintainability, and speed given real-world constraints.
• Clear communicator: comfortable explaining trade-offs and technical decisions to both engineering peers and non-technical stakeholders.
• Collaborative and team-oriented: seeks input early, shares context proactively, and works effectively across engineering, analytics, and business teams.
• Ownership mentality with accountability for the reliability and impact of deployed AI solutions.
• Intellectually curious with a genuine interest in applying Generative AI and ML to real enterprise problems — not just passing familiarity with the technology.
• Comfortable with ambiguity: able to make progress without every requirement being fully defined, while knowing when to flag uncertainty.
• Adaptability and comfort operating in fast-moving, evolving technical environments.
• Strong attention to detail and commitment to building high-quality, user-centered solutions

Oliver Wyman is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective. For more information, visitoliverwyman.com, or follow us onLinkedInandX.

Marsh is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.

Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one "anchor day" per week on which their full team will be together in person.

R_352810