Top Machine Learning Jobs in Mexico City

The Salesforce Developer will design scalable solutions, write code using Apex and LWC, leverage AI for productivity, and ensure application performance while collaborating with various stakeholders.

The Promoter Agencies Manager will oversee field marketing agencies to drive customer engagement and sales through a national demand generation strategy. Responsibilities include managing agency relationships, analyzing marketing metrics, and coordinating marketing initiatives.

The Python Engineer will design and develop AI/ML-driven applications, optimizing models, collaborating with teams, and integrating AI features while working with diverse data types.

Build, maintain, and improve programming solutions and AI/ML applications to automate workflows, analyze operational data, and optimize processes. Collaborate with cross-functional teams, maintain operational systems, and document technical procedures to support reliable, scalable operations.

As a Data Scientist at Zicasso, you will analyze complex datasets, develop predictive models, and optimize customer experiences using AI and machine learning techniques, while collaborating with cross-functional teams.

The Research Engineer will build training infrastructure and reward pipelines, fine-tune models, bridge RL with LLMs, run fast experiments, communicate findings, and collaborate with the research team at Firecrawl.

Design, build, and deploy end-to-end data science solutions for pricing, underwriting, claims, and operations. Perform analyses on structured and unstructured data, develop and monitor ML models, create dashboards and visualizations, implement data pipelines, and collaborate cross-functionally to prioritize analytics initiatives and communicate results to technical and non-technical stakeholders.

The Global Privacy Counsel will lead the data protection strategy for TaskUs, ensuring compliance with global privacy laws, overseeing privacy initiatives across product development, managing third-party vendors, and leading the privacy team.

The Senior AI/ML Engineer will architect and develop AI/ML solutions, collaborate with teams, and ensure deployment compliance, especially in healthcare.

Manage a team of cloud engineers and architects, oversee data delivery engagements, handle technical responsibilities, and interface with clients for technical improvements.

The VP of Product Development will lead a global engineering team for eDiscovery platforms, focusing on modernization, reliability, and AI integration while managing execution and stakeholder communication.

The Solutions Consultant will engage with customers to deploy Snowflake solutions, enhance internal tooling, and deliver outcomes for modernization projects.

Design and implement scalable Generative AI architectures, build Python services, integrate LLMs, oversee deployment operations, and evaluate emerging technologies.

The IT Consultant will design finance processes, lead automation initiatives, and manage SAP FI/CO solutions while ensuring project readiness and ongoing support.

Lead the design and optimization of data engineering solutions, collaborating with teams to deliver projects using modern technologies and AI frameworks.

Design, build, and maintain scalable data and ML pipelines, manage vector stores and RAG workflows, integrate LLM frameworks for training and inference, enable semantic search and AI-driven BI, and document data processes while partnering with stakeholders.

The Machine Learning Engineer / Data Scientist will build and deploy machine learning solutions, translating business needs into ML problems, working with data analysis, model development, and productionization tasks.

Design, train, and refine machine learning algorithms on high-dimensional data to produce practical predictive models. Apply random forests, deep learning, generative modeling, and neural-network techniques to improve NLP and machine perception for real-world applications.

Build and own production ML and LLM systems for onboarding, risk, compliance, and operations. Design models, engineer features from messy data, balance performance and interpretability, integrate with backend training/inference pipelines, write tested code, and partner with product and operations to drive scalable, reliable automation.

The role involves developing and maintaining a robust ML/AI platform, focusing on infrastructure, autonomous agents, CI/CD pipelines, and AI-native tooling, with responsibilities across engineering best practices.

Lead technical client engagements in machine learning and AI, shape strategy, influence architecture, and ensure high-quality outcomes for clients by leveraging AWS services.

Lead a team of ML engineers, manage client engagements, and provide strategic advisory on ML architecture while driving pre-sales. Ensure team growth and quality of deliverables.