Sobre este puesto de Oliver Wyman - ML Engineer - Generative AI & Unstructured Data - Mexico City en Marsh
Company:
Oliver WymanDescription:
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 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
