Sr. AI/ML Engineer (MXC) at AlertMedia

EducationPals; México; Remote; MXN 2,241,000 - 2,988,000; Full time

Job summary

AlertMedia is seeking a hands-on Sr. AI/ML Engineer to lead the evaluation, training, and integration of advanced AI and machine learning models across our product suite.

You will help shape our AI strategy by selecting appropriate models for use cases, and optimize performance through fine-tuning and prompt engineering, while ensuring responsible deployment of AI systems within our products.

Qualifications

• AI fundamentals.
• Machine learning.
• Data science.
• Prompt engineering.
• LLM evaluation.

Responsibilities

• Lead the evaluation, training, and integration of advanced AI and ML models across the product suite.
• Shape our AI strategy by identifying the most appropriate models for our use cases.
• Optimize model performance through fine-tuning and prompt engineering.
• Ensure responsible deployment and monitoring of AI systems within products.

Skills

AI fundamentals

Machine learning

Data science

Prompt engineering

LLM evaluation

Job description

AlertMedia is looking for an innovative and hands-on Sr. AI/ML Engineer with a strong background in applied machine learning and data science to lead the evaluation, training, and integration of advanced AI and machine learning models across our product suite. In this role, you will help shape our AI strategy by identifying the most appropriate models for our use cases, optimizing their performance through fine-tuning and prompt engineering, and ensuring their responsible deployment within our p

Relevance is based on skills and concepts — not a guarantee that you qualify.

Covered skills: AI fundamentals Machine learning Data science Prompt engineering LLM evaluation

Skills to close: None listed

Build the skills for this job

Coming-soon courses matched to this posting — outline, waitlist, and skill path.

1. Specification Failures in LLM Systems

Understand why user instructions fail and how to distinguish specification errors from model errors.

Learn to compare user requests against available evidence and establish grounded task boundaries.

3. Multi-Stage Workflow Architecture

Design LLM pipelines that separate task repair from task execution across validation, refinement, and generation stages.

4. Aspect Refinement for Summarization

Build systems that validate, merge, remove, and add aspects before generating aspect-based summaries.

Implement methods to fix incomplete, conflicting, or misaligned user requests before execution.

6. Domain-Specific Validation Patterns

Apply instruction validation to financial analysis, product research, compliance, and enterprise search use cases.

7. Evaluation and Production Deployment

Measure instruction validation effectiveness and deploy grounded LLM systems safely at scale.

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