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About This Role
Miami, Florida · Full\-time · Senior
#### About The Position
We are seeking an Data Modeling \& Analytics to join our growing Data \& Analytics team. This role sits at the intersection of data engineering, analytics, and AI, transforming raw data into trusted, scalable, and well\-documented data products that drive decision\-making across the business.
As an Analytics Engineer, you will design and maintain data models within our cloud data warehouse, develop ELT pipelines, and build data marts that support reporting, self\-service analytics, data science, and emerging AI initiatives. You will partner closely with business stakeholders, analysts, and engineers to ensure our data ecosystem is accurate, accessible, and optimized for growth.
This is a global role supporting data initiatives across the United States, United Kingdom, and future markets. We are building an AI\-forward organization, and you will be expected to leverage AI\-powered development tools to enhance productivity, improve data quality, and accelerate delivery.
Data Modeling \& Analytics
- Design, develop, and maintain scalable dbt models that power reporting and analytics across the organization.
- Build and manage data marts that support business intelligence, self\-service analytics, predictive analytics, and AI\-driven insights.
- Partner with analysts and business stakeholders to translate reporting and operational requirements into reliable, reusable data models.
- Develop and maintain a consistent semantic layer that enables accurate reporting and decision\-making.
Data Pipelines \& Engineering
- Build, manage, and troubleshoot ELT pipelines and integrations using Airbyte and related technologies.
- Collaborate with Data Engineering teams to maintain a reliable end\-to\-end data platform spanning data ingestion, warehousing, transformation, and reporting.
- Support onboarding of new B2B clients by developing client\-specific data models, transformations, and workflows.
- Monitor and optimize warehouse performance, ensuring data processing efficiency and cost effectiveness.
Data Quality \& Governance
- Implement automated testing, data validation, and quality monitoring using dbt and related tools.
- Maintain data documentation, lineage, metadata, and business definitions to promote data trust and transparency.
- Contribute to data governance initiatives and help establish best practices for data management across the organization.
AI \& Innovation
- Design data structures optimized for AI and machine learning use cases.
- Leverage AI\-assisted development tools such as Cursor, Claude, and similar technologies to accelerate development, testing, and documentation.
- Help champion AI\-enabled workflows and best practices across the Data \& Analytics organization.
Qualifications
Required
- 2\-4 years of experience in Analytics Engineering, Data Engineering, Business Intelligence Engineering, or a similar role.
- Strong experience with dbt for data modeling, transformation, testing, and documentation.
- Advanced SQL skills with experience developing complex analytical datasets.
- Experience with cloud data warehouses such as Amazon Redshift, Snowflake, BigQuery, or equivalent platforms.
- Working knowledge of Python for data processing, automation, or analytics workflows.
- Experience building and maintaining ELT/ETL pipelines using tools such as Airbyte, Fivetran, or similar platforms.
- Understanding of dimensional modeling, star schemas, and analytics\-focused data architecture.
- Experience using Git and collaborative software development practices.
- Familiarity with business intelligence and visualization tools, preferably Tableau.
- Comfortable leveraging AI\-powered development tools and assistants to improve productivity and workflow efficiency.
- Strong communication and stakeholder management skills.
- Must be based in the United States.
- Bachelor's degree in Computer Science, Information Systems, Statistics, Engineering, or a related field, or equivalent practical experience.
Preferred
- Experience within healthcare, health technology, or other regulated industries.
- Experience working with HIPAA\-, GDPR\-, or other compliance\-regulated data environments.
- Experience supporting client\-facing data integrations and B2B customer onboarding.
- Experience in high\-growth startup or fast\-paced environments.
- Exposure to machine learning, predictive analytics, or AI\-powered data applications.
Why Join Us?
- Build the data foundation for a rapidly growing global organization.
- Work with modern technologies including dbt, Redshift, Airbyte, Tableau, and AI\-assisted development tools.
- Help shape how data, analytics, and AI are leveraged across the business.
- Collaborate with cross\-functional teams on impactful, high\-visibility initiatives.
- Influence the future of self\-service analytics and AI\-driven decision\-making at scale.
Why Join Us?
You’ll work with a talented global team, contribute to meaningful technical initiatives, and help shape a high‑performing DevOps culture.
Matrix is a global, dynamic, and fast‑growing technology services and consulting company with over 17,000 employees worldwide. Founded in 2001, Matrix has grown through significant organic expansion and strategic acquisitions, executing some of the largest and most impactful technology projects in the market.
We specialize in the design, development, and implementation of advanced technologies, software solutions, and digital products. Our services include infrastructure and consulting, IT outsourcing and offshore delivery, training and assimilation programs, and acting as a trusted implementation and delivery partner for the world’s leading software vendors.
With deep expertise across both the private and public sectors—spanning Finance, Telecom, Healthcare, Hi‑Tech, Education, Defense, and Security—Matrix serves a strong customer base in Israel alongside a continuously expanding global client portfolio.
At Matrix, our people are at the heart of our success. We are a community of talented, creative, and dedicated professionals who are passionate about innovation and excellence. We actively attract, develop, and retain top talent, recognizing that every employee’s contribution is critical to our continued growth and future success.
Matrix’s success is built on a challenging and engaging work environment, competitive compensation and benefits, and meaningful career development opportunities. We foster a diverse, inclusive culture that encourages collaboration, continuous learning, and growth—together.
Join the winning team. Be challenged, grow your career, and enjoy the journey in a highly respected global organization.
To learn more, visit: www.matrix\-ifs.com
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 Matrix International Financial Services, 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.
Matrix International Financial Services AI Hiring
Matrix International Financial Services has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Miami, FL, US, 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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