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About This Role
Description
Infor is seeking a Senior AI Business Analyst to join AI Studio, one of Infor's most strategic initiatives. AI Studio is transforming how solutions are created across the Infor ecosystem, with a vision that every business user on Infor OS can describe a problem in plain language and receive a fully configured, component-assembled, and packaged solution ready for deployment to any customer environment, without writing code or reading documentation.
In this role, you will work closely with Product, Engineering, Architecture, and domain experts to translate business requirements into AI-driven solutions and help bring this vision to life.
A Typical Day in the Life Includes:
- Partner with Product Management, Engineering Leads, and Architecture teams to define, document, and prioritize business requirements and AI Studio roadmap initiatives.
- Translate complex ERP business requirements into structured specifications that can be executed through agentic AI workflows and orchestration frameworks.
- Analyze existing CloudSuite Distribution (SX.e) and Infor OS business processes to identify opportunities for AI-driven automation, intelligent recommendations, and implementation acceleration.
- Collaborate closely with Forward Deployed Engineers (FDEs), Solution Architects, and domain experts to validate generated solutions against real-world customer workflows.
- Lead discovery workshops and cross-functional alignment sessions across Infor OS product teams.
- Define acceptance criteria, validation frameworks, and test strategies to ensure AI-generated configurations are accurate, compliant, explainable, and audit-ready.
- Own business process documentation, process maps, traceability matrices, solution playbooks, and governance artifacts for AI Studio initiatives.
- Serve as the voice of the customer, influencing product direction by identifying gaps between generated solutions and actual business needs.
- Define and monitor business success metrics such as adoption, implementation velocity, configuration effort reduction, and solution accuracy.
- Mentor junior analysts and drive requirements management best practices across AI Studio.
Basic Qualifications:
- 5+ years of Business Analysis experience in enterprise software, SaaS, or digital transformation environments.
- Strong expertise in requirements gathering, process analysis, documentation, and stakeholder management.
- Experience working across Product, Engineering, and Architecture teams.
- Excellent workshop facilitation, communication, and collaboration skills.
- Familiarity with AI, Generative AI, Large Language Models (LLMs), AI assistants, or AI-enabled business solutions.
- Ability to translate complex business requirements into structured functional specifications and solution definitions.
Preferred Qualifications:
- Experience with ERP solutions such as Infor, SAP, Oracle, Microsoft Dynamics, NetSuite, Epicor, or similar platforms.
- Knowledge of CloudSuite Distribution (SX.e), supply chain, distribution, or enterprise operations processes.
- Exposure to intelligent automation, workflow orchestration, or AI implementation projects.
- Familiarity with AWS cloud services.
- Experience with AI development tools and platforms such as Claude, ChatGPT, Microsoft Copilot, or similar technologies.
- Experience working in agile product development environments.
About Infor
About Infor Infor is where ambition meets impact. Join a global community of bold thinkers and innovators, where your expertise doesn't just solve problems. it shapes industries, unlocks opportunities, and creates real-world impact for billions of people. At Infor, you're not just building a career. you're helping to build what's next.
Infor is a global leader in business cloud software products for companies in industry specific markets. Infor builds complete industry suites in the cloud and efficiently deploys technology that puts the user experience first, leverages data science, and integrates easily into existing systems. Over 60,000 organizations worldwide rely on Infor to help overcome market disruptions and achieve business-wide digital transformation.
For more information visit www.infor.com About Infor Infor is where ambition meets impact. Join a global community of bold thinkers and innovators, where your expertise doesn't just solve problems. it shapes industries, unlocks opportunities, and creates real-world impact for billions of people. At Infor, you're not just building a career. you're helping to build what's next.
Infor is a global leader in business cloud software products for companies in industry specific markets. Infor builds complete industry suites in the cloud and efficiently deploys technology that puts the user experience first, leverages data science, and integrates easily into existing systems. Over 60,000 organizations worldwide rely on Infor to help overcome market disruptions and achieve business-wide digital transformation.
For more information visit www.infor.com
Our Values At Infor, we strive for an environment that is founded on a business philosophy called Principle Based Management™ (PBM™) and eight Guiding Principles: integrity, stewardship & compliance, transformation, principled entrepreneurship, knowledge, humility, respect, self-actualization.
We have a relentless commitment to a culture based on PBM™. Informed by the principles that allow a free and open society to flourish, PBM™ prepares individuals to innovate, improve, and transform while fostering a healthy, growing organization that creates long-term value for its clients and supporters and fulfillment for its employees.
Infor is an Equal Opportunity Employer. We are committed to creating a diverse and inclusive work environment. Infor does not discriminate against candidates or employees because of their sex, race, gender identity, age, sexual orientation, religion, national origin, veteran status, or any other protected status under the law. If you require accommodation or assistance at any time during the application or selection processes, please submit a request by following the directions located in the FAQ section.
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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 Infor, 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. Senior-level AI roles across all categories have a median of $228,700.
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.
Infor AI Hiring
Infor has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Atlanta, GA, US, Atlanta, 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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