Oliver Wyman - ML Engineer - Generative AI & Unstructured Data - Mexico City
Marsh & McLennan Companies2 hours ago
Mexico City, MexicoMid Level
Responsibilities
- Design and build scalable ML pipelines for unstructured data including documents, transcripts, images, logs, and free-form text.
- Develop and maintain production AI services and APIs for internal applications and analytics platforms.
- Productionize ML and generative AI solutions through packaging, containerization, deployment, versioning, and lifecycle management.
- Build RAG, embedding, semantic search, and LLM-powered API workflows.
- Implement monitoring, observability, model performance tracking, and SLA-focused reliability practices.
- Collaborate with data scientists to transition experimental models into stable production environments.
- Contribute to CI/CD, 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 technical decisions for maintainability and extension.
Requirements
- At least 3 years of professional experience in ML engineering, applied AI, data science, or related engineering roles focused on production systems.
- Hands-on experience with unstructured data solutions such as NLP pipelines, document processing, OCR, text classification, embeddings, entity extraction, or semantic search.
- Proficiency in Python and SQL, with experience using PySpark and Databricks or similar distributed compute platforms.
- Experience developing and deploying production ML model-serving APIs using FastAPI or similar frameworks.
- Familiarity with MLOps, CI/CD for ML, model monitoring, automated retraining, Docker, and Git-based workflows.
- Experience with cloud-based AI/ML platforms and enterprise deployment environments.
- Strong software engineering fundamentals in readable code, testing, debugging, and performance optimization.
- Preferred experience with OpenAI, Azure OpenAI, or open-source models; LangChain or LlamaIndex; Azure AI Search, Pinecone, or pgvector; and responsible AI practices.
- Ability to communicate technical decisions, collaborate across teams, exercise engineering judgment, and work independently in ambiguous environments.
Benefits
- Hybrid work arrangement with flexibility to work remotely, with colleagues expected to work from their local office or onsite with clients at least three days per week.
- Office-based teams identify at least one weekly anchor day for the full team to work together in person.
- Diverse, inclusive, and flexible work environment with collaboration, connection, and professional development benefits.
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