Top Machine Learning Jobs in Mexico

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

Lead complex AI and analytics projects using Palantir Foundry to translate Finance and Lead Gen needs into operationalized ML solutions. Work across data modeling, mining, visualization, and model implementation; validate results with stakeholders, improve data quality, and document models for reuse and scale.

Lead Salesforce product initiatives for Channel and Enterprise sales: gather and document requirements, build roadmaps, define solutions, drive implementation, enable adoption, produce reports, and partner with sales, sales ops, IT and enablement to improve selling effectiveness and reduce CRM admin burden.

Own and set technical direction for the Everand backend, building scalable, reliable systems for discoverability, personalization, and core consumption journeys. Drive architecture, platform health (CI/CD, staging, DBs, queues, cost, observability), mentor engineers, lead cross-team planning, modernize legacy systems, and partner with product, design, analytics, web/mobile, and ML to support experimentation and telemetry-driven decisions.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

As a Senior Data Engineer, you will design and maintain scalable data pipelines, collaborate on machine learning models, and support analytics initiatives for various business teams.

Lead the design, development, and production deployment of machine learning models and predictive analytics pipelines. Collaborate with engineering, product, analytics, and data teams to optimize cloud-based data solutions, automate workflows, and apply statistical modeling to business challenges. Mentor junior data scientists, establish MLOps best practices, and communicate insights and recommendations to stakeholders and executives.

Manage and optimize site search across enterprise websites by analyzing search terms, authoring search rules, collaborating with product/engineering and content teams, developing analytics and governance, training stakeholders, and proposing enhancements to improve search relevancy and performance.

Configure and maintain containerized ML environments, execute and monitor distributed training workloads on accelerator clusters, and automate operations with Python and Bash. Troubleshoot failures through log collection and infrastructure analysis, document findings, track execution status, communicate test progress, and collaborate with engineering teams to improve reliability and operational efficiency.

Develops and supports data and machine learning activities, including data analysis, quality management, database administration, reporting, and processing millions of records in data lakes. Uses Python and PySpark, applies software design patterns and development standards, and may support CI/CD tooling. Performs routine operational deliverables, assesses risk, follows compliance requirements, and communicates findings and recommendations across related teams.

Own the architecture, deployment, reliability, and cost optimization of production AI/ML infrastructure on GCP. Build model serving systems, reproducible ML pipelines, GitOps workflows, and multi-tenant Kubernetes platforms. Manage LLM and agentic workloads, Terraform infrastructure, observability, SLOs, drift monitoring, accelerator capacity, and inference costs. Define operational standards, improve automation, and guide platform evolution for scalable enterprise-grade ML systems.

Industry Expert who reviews advanced AI and systems engineering projects, leads technical defenses and mock architecture review boards, hosts occasional design sessions, and sets technical standards for instructors. The role requires senior-level production engineering experience, strong architectural judgment, the ability to critique systems live, and expertise in AI/ML engineering, AI systems, or forward-deployed engineering. This is a fully remote commitment of 4–10 hours monthly with negotiable hourly compensation.

Combine industry research and market analysis for oil supply with technical ownership of data, modelling and automation workflows. Build, refactor and automate data pipelines, improve legacy codebases, support forecasting models and client-facing research, and apply modern data tooling and AI to enhance analytical workflows.

Work with the data science team to understand insurance data and fraud, build and optimize mathematical models and algorithms, implement production-quality code, participate in client workshops to gather feedback, and propose continuous improvements.

Designs and optimizes Retrieval-Augmented Generation systems, including document ingestion, chunking, embeddings, retrieval, reranking, vector search, and source-grounded responses. Develops evaluation frameworks to measure relevance, citation accuracy, factual grounding, latency, cost, and hallucination reduction. Collaborates cross-functionally to deliver scalable, trustworthy, and responsible Generative AI solutions for legal and professional information products. Preferred work includes AI agents, LLM workflows, hybrid search, observability, and regulated-domain applications.

The CX Tools & Automation Analyst will automate customer support tickets using APIs, optimize authentication methods, and enhance tooling efficiency while collaborating with stakeholders and documenting changes.

This role involves leading strategic planning, product innovation, and cross-functional collaboration to enhance pharmacy analytics and drive growth initiatives for CVS Health.

Design, build, and maintain scalable data pipelines and architectures to ingest, store, and analyze industrial OT/IT data (PLC, SCADA, IoT). Ensure ETL/ELT processes, data quality, governance, and performance optimization to support OEE, KPIs, BI, and ML initiatives. Collaborate with engineering, data science, and BI teams across the full project lifecycle.

As a Senior Machine Learning Engineer, you will develop and maintain infrastructure for ML applications, collaborate across teams, and drive tool development for data processing and model deployment.

Create and review realistic, locale-specific robotics and natural sciences tasks in Spanish (LATAM), apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

Lead full presales cycle for cloud, data, and AI deals; design AWS-based solutions; manage pipeline in Salesforce; engage CTOs and execs; develop GTM assets; mentor and grow a high-performing team; drive US cultural and technical process adoption across regional branches.

Responsible for creating and maintaining brand- and item-level consensus depletion forecasts by partnering with Marketing, Innovation, Finance and Operations. Translate portfolio and innovation plans into demand outlooks, quantify demand drivers and risks, apply statistical modeling and analytics, present forecast recommendations, and drive cross-functional alignment and continuous improvement in demand planning processes.

Operate and monitor AI models and agent systems in production. Track performance, drift, latency, errors, availability, and business impact; manage incidents, rollbacks, deployments, versioning, registries, and lifecycle transitions. Maintain governance, audit trails, risk controls, fairness, bias, explainability, and transparency monitoring. Integrate cloud, container, monitoring, logging, alerting, and CI/CD platforms while collaborating with product, engineering, data science, and risk teams.

Develop and validate change-detection and anomaly models on multi-temporal Sentinel imagery for pipeline corridors. Fine-tune geospatial foundation models with LoRA/PEFT, implement SAR displacement methods and dune-migration tracking, engineer data ingestion from Copernicus/STAC sources, design labelling and validation strategies, and communicate results to technical and business stakeholders.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.