Interested in this AI/ML Engineer role at IFS?
Apply Now →About This Role
Company Description
IFS is a billion\-dollar revenue company with 7000\+ employees on all continents. Our leading AI technology is the backbone of our award\-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting\-edge.
At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact. We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust.
We celebrate diversity and understand our responsibility to reflect the diverse world we work in. We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view.
By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world.
We’re looking for innovative and original thinkers to work in an environment where you can \#MakeYourMoment so that we can help others make theirs. With the power of our AI\-driven solutions, we empower our team to change the status quo and make a real difference.
If you want to change the status quo, we’ll help you make your moment. Join Team Purple. Join IFS.
Job Description Purpose
As an AI Solution Architect, you are part of the industry’s most formidable solution engineering force. You work across the IFS AI portfolio, applying deep domain expertise within ERP, EAM, or FSM (in some cases more than one of these disciplines) to win strategic deals, elevate presales performance, and drive go\-to\-market innovation across regions and market units.
You are a senior technical specialist and trusted advisor who leads high\-impact strategic pursuits at the intersection of the AI portfolio and your domain. You bring the AI solution set to life in the language of ERP, EAM, and FSM buyers shaping our solution engineering tactics, raising the quality bar across presales, and enabling key talent across market units in the core business. You collaborate broadly across Sales, Marketing, R\&D, and GTM functions to strengthen execution and accelerate innovation. In short: this role is about representing and developing our AI market leadership through the presales function, grounded in real industry and domain depth.
Key Responsibilities
Technical Win Pursuits / Sales Execution
Lead from the front in qualified, high\-impact AI pursuits by applying solution, AI portfolio, and industry depth, tailoring demonstrations and delivering proof\-of\-value engagements that drive customer confidence. Set the quality bar and avoid scope creep. This role has a key emphasis on this activity: Ensuring technical win pursuit excellence across the pipeline.
AI Portfolio Depth \& Domain Translation
Act as the connective tissue between the AI portfolio and your domain (ERP, EAM, and/or FSM). Translate AI capabilities into credible, domain\-specific outcomes and architectures, and ensure solutions are technically sound and implementable.
Trusted Advisor Across the Business
Provide insight\-based guidance to shape marketing campaigns, sales plays, messaging, roadmap priorities, and competitive positioning for the AI portfolio within your domain.
Presales Performance Elevation
Strengthen presales execution through key content creation, storytelling, reusable asset development, mentoring, and setting the example in delivery excellence.
GTM Innovation
Drive evolution in our AI go\-to\-market model through cross\-functional collaboration, industry\-specific sales plays, and field innovation. The emphasis will always be on strategic deal execution, but the balance between these focus areas may vary depending on individual expertise and evolving business needs.
Subject Matter Expertise
The AI Solution Architect carries deep domain expertise in one or more of the following, paired with strong command of the IFS AI portfolio:
- ERP\-centric: Deep understanding of enterprise resource planning processes, market dynamics, and the competitive landscape.
- EAM\-centric: Deep understanding of enterprise asset management (EAM), APM, and AIP market dynamics.
- FSM\-centric: Deep understanding of field service software market dynamics, including products, competitors, customers, and partners.
- Multi\-domain: Several architects will operate credibly across two or more of these disciplines.
Success Metrics
- Contribution to AI portfolio revenue targets in partnership with sales leadership.
- Measurable improvement in presales readiness and win rates on qualified opportunities.
- Quality and reuse of solution assets, demonstrations, and proof\-of\-value engagements.
- Field\-sensed product feedback that fuels future AI portfolio innovation.
Qualifications Ideal Candidate Profile
- Self\-Starter and Fast Learner: Thrives in a high\-paced, dynamic business environment. Learns quickly and adapts with agility.
- Subject Matter Expertise: Deep domain credibility in ERP, EAM, and/or FSM, combined with genuine fluency in applied AI.
- Strong Team Player: Acts as both player and coach. Builds community, shares knowledge, and mentors rising talent across geographies.
- Presentation and Demonstration Skills: Skilled in delivering both software demonstrations and executive\-level presentations, with the ability to tailor delivery to varied audiences.
- Technical Skills: Solid grasp of business and IT architecture; capable of assessing customer technology landscapes in the context of AI\-led business transformation.
- Global Readiness: Fluent in English, with additional language skills seen as an advantage. Willingness to travel internationally and work flexible hours to support global engagements is essential.
- Interpersonal Skills: Creative, curious, and persuasive communicator, capable of influencing direction across technical and non\-technical stakeholders at all levels.
- AI\-first worker: Working “AI\-first”, applying AI techniques to become both more efficient in everyday work, and more effective in customer engagements. Naturally curious and continuously finds new approaches and solutions.
Additional Information What We're Offering:
- Salary Range: $150,000\-$170,000 \+ bonus
- Flexible paid time off, including sick and holiday
- Medical, dental, \& vision insurance
- 401K with Company contribution
- Flexible spending accounts
- Life insurance and disability benefits
- Tuition assistance
- Community involvement and volunteering events
*We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.*
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. VEVRAA Federal Contractor, 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, or status as a protected veteran. VEVRAA Federal Contractor, Equal Opportunity Employer
Salary Context
This $150K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 IFS, 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 in Demand for This Role
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. This role's midpoint ($160K) sits 26% below the category median. Disclosed range: $150K to $170K.
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.
IFS AI Hiring
IFS has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Itasca, IL, US. Compensation range: $170K - $200K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.