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About This Role
- Alpharetta / Atlanta
- United States of America
- Other
- Full time
- 9/30/2026
- J00179159
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Equifax is where you can power your possible. If you want to achieve your true potential, chart new paths, develop new skills, collaborate with bright minds, and make a meaningful impact, we want to hear from you.
We seek a dynamic and driven individual with a strong technical foundation to serve as a Generative AI Engineer. You will leverage our data, technology, and analytics to build LLM pipelines that generate actionable insights for our business and customers.
This key position involves working with large data sources on Google Cloud Platform (GCP) and Google AI Platform, designing, developing, and implementing cutting-edge generative AI solutions on Vertex AI.
This is a unique opportunity to shape the future of AI within the company, driving innovation by leveraging our cloud-native technology (GCP/Vertex AI). Strong communication skills are required, and financial industry experience is preferred. This position provides an opportunity to work with best-in-class technology and a rich big data ecosystem.
Location & Logistics:
- This role requires being in either our Alpharetta or Atlanta, Georgia office 4 days/week on Mon - Thurs.
- This position does not offer immigration sponsorship (current or future) including F-1 STEM OPT extension support.
What you'll do
- Design and develop key components of GenAI solutions on Google Cloud Vertex AI to generate insights from structured and unstructured data, and collaborate with engineering teams on deployment.
- Integrate diverse data sources, data types, and data structures into AI / LLM based solutions, using data and technology best practices.
- Implement prompt engineering and RAG (Retrieval-Augmented Generation) strategies to improve response accuracy.
- Integrate GenAI capabilities into data pipelines, dashboards, and APIs for real-time analytics.
- Understand how business requests translate into AI-enabled analytical and technical solutions.
- Develop and maintain a deep understanding of the latest advancements in AI, particularly in generative AI and its applications in data analysis and insight generation.
- Communicate complex technical concepts clearly and concisely to both technical and non-technical audiences.
What experience you need
- Bachelor’s degree or higher in Computer Science or Computer Engineering, Statistics, Mathematics, or a related quantitative field.
- Hands-on experience developing and building Generative AI or LLM-based solutions.
- 1+ years of experience working within modern cloud environments.
- Proficiency in Python and AI frameworks (e.g., LangChain).
- Familiarity with consuming REST APIs and working with lightweight web frameworks (e.g., Flask, FastAPI).
- Ability to learn and apply new technologies; passion for staying abreast of the latest advancements in Generative AI research and technology; passion for AI research and a strong desire to contribute to cutting-edge projects.
- Strong communication skills to present analytical results to technical and non-technical audiences alike.
What could set you apart
- Master’s degree in a related field is a strong plus.
- Experience contributing to production-grade software or enterprise deployments.
- Experience with additional programming languages such as Java or C/C++.
- Exposure to Vertex AI, GCP AI/ML services (AutoML, BigQuery ML, Cloud Run, etc.) or a similar cloud technology.
- Strong foundational skills in Linux Operating System.
- Understanding of NLP, deep learning, and generative architectures (Transformers, Diffusion Models, etc.).
- Background in credit risk, financial data analytics, or risk modeling.
- Experience working with large datasets on a big data platform (e.g., Google Cloud, AWS, Snowflake, Hadoop).
- Experience in Business Intelligence, data visualization, and customer insights generation.
- Familiarity with data governance, model bias mitigation, and regulatory frameworks (GDPR, AI Act, SEC compliance).
- Experience with MLOps practices, model monitoring, and CI/CD for AI workflows.
- Knowledge of prompt tuning, fine-tuning, and parameter-efficient methods (LoRA, PEFT).
- Hands-on experience with RAG, multi-modal AI, and hybrid AI architectures.
- Contributions to the AI community through publications, open-source projects, or conference presentations.
We offer comprehensive compensation and healthcare packages, 401k matching, paid time off, and organizational growth potential through our online learning platform with guided career tracks.
Are you ready to power your possible? Apply today, and get started on a path toward an exciting new career at Equifax, where you can make a difference!
Who is Equifax?
At Equifax, we believe knowledge drives progress. As a global data, analytics and technology company, we play an essential role in the global economy by helping employers, employees, financial institutions and government agencies make critical decisions with greater confidence.
We work to help create seamless and positive experiences during life’s pivotal moments: applying for jobs or a mortgage, financing an education or buying a car. Our impact is real and to accomplish our goals we focus on nurturing our people for career advancement and their learning and development, supporting our next generation of leaders, maintaining an inclusive and diverse work environment, and regularly engaging and recognizing our employees. Regardless of location or role, the individual and collective work of our employees makes a difference and we are looking for talented team players to join us as we help people live their financial best.
Equifax is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Equifax, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Equifax AI Hiring
Equifax has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Alpharetta, GA, US, Atlanta, GA, US, Alpharetta, GA.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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