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About This Role
Location: Ciudad de Mexico CDMX (Mexico City), MX, USA
Shift: 1st Shift
Work model: Remote
Schedule: FULLTIME; Monday\-Friday, 8a.m.\-5p.m.
Job Description
AI/ML Engineer II designs, develops, deploys and maintains applications which enable AI\-driven solutions that solve complex business problems. In this role, you will take on complex projects and responsibilities, working closely with senior engineers and data scientists to develop and maintain software solutions for data science initiatives. This is an intermediate level individual contributor position ideal for professionals with some experience in the field who are ready to take the next step in their career and contribute to impactful data science initiatives.
ESSENTIAL JOB DUTIES AND RESPONSIBILITIES:
- Collaborate with data scientists and engineers to collect requirements, design, develop, deploy and support data\-driven software solutions.
- Work with stakeholders to identify business opportunities, discuss challenges and contribute to developing solutions.
- Lead the development and optimization of data processing and feature engineering pipelines.
- Deploy, fine\-tune and monitor machine learning models and algorithms in production both on\-premises and in cloud environments.
- Build, optimize and maintain APIs and microservices for serving machine learning models.
- Implement model performance monitoring and drift detection.
- Write high\-quality, efficient, and well\-documented code.
- Conduct code reviews and mentor junior engineers on best practices for software development.
- Perform complex data analysis and generate insightful reports and visualizations to support strategic business decisions.
- Troubleshoot and resolve sophisticated software and data issues.
- Self\-manage multiple projects, priorities, and timelines.
- Contribute to the continuous improvement of development processes and methodologies.
- Effectively communicate progress and conflicts with management, technical leads, and stakeholders.
- Contribute to the advancement of internal software platforms and packages by researching and adopting purchased and open\-source products.
- Stay current with the latest industry trends, technologies, and best practices in data science and software engineering.
- Ensure compliance with data privacy, security, and governance standards.
- Take part in team's on\-call rotation.
- This description is not an exhaustive or comprehensive list of all job responsibilities, tasks, and duties.
- Other duties and responsibilities may be assigned, and the scope of the job may change as necessitated by business demands.
- Maintain regular and consistent attendance and timeliness.
- Exhibit behavior in alignment with our core values at all times.
- Ability to work routinely on\-site at a Schneider facility.
SPECIALIZED KNOWLEDGE:
- Bachelor's degree in Computer Science, Software Engineering, Data Science, Engineering, Information Systems or a related field.
- 2\+ years of experience in software engineering, data science or a related field in information technology.
- Proficient in programming languages such as Python, Java, or C\+\+.
- Strong knowledge of data science libraries and frameworks (e.g., Pandas, NumPy, Scikit\-learn).
- Solid understanding of machine learning concepts, algorithms, model evaluation metrics, and feature engineering.
- Experience with SQL and database management.
- Proficiency with version control systems (e.g., Git).
- Familiarity with cloud platforms (e.g., Azure, AWS, Google Cloud) and big data technologies.
- Familiarity with container platforms (e.g., Docker, Kubernetes).
- Familiarity with streaming platforms (e.g., Apache Kafka).
- Familiarity with ML Ops tools (e.g., MLFlow,) and CI/CD concepts.
- Experience developing applications in a Linux server environment preferred.
- Ability to create advanced visualizations with commercial software (e.g., Power BI) or open\-source tools (e.g., Matplotlib, Seaborn, Plotly).
- Excellent problem\-solving skills and attention to detail.
- Knowledge of advanced analytics software technologies, technical design, programming, monitoring, basic engineering modeling and troubleshooting enabling software related technologies. Also has knowledge of advanced analytics strategies, architectural standards and best practices.
- Business and technical experience in data analytics, application development, or operations research.
- Strong communication and teamwork abilities.
- Demonstrated ability to learn and adapt in a fast\-paced environment.
- Transportation and Logistics experience a plus.
SKILLS/BEHAVIORS NECESSARY TO PERFORM JOB:
- Reference compass for Exempt Individual Contributor enterprise behavior definitions and examples that align to Core Competencies.
PI286235001
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Schneider, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Schneider AI Hiring
Schneider has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mexico, MO, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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