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About This Role
Company Description
IFS is a billion\-dollar revenue company with 7000\+ employees on all continents. We deliver award\-winning enterprise software solutions through the use of embedded digital innovation and a single cloud\-based platform to help businesses be their best when it really matters–at the Moment of Service™.
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 accept that there are so many different perspectives in this world. 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.
If you want to change the status quo, we'll help you make your moment. Join Team Purple. Join IFS.
Job Description
Our Aerospace \& Defense business helps the world's leading airlines, MROs, OEMs and defense operators keep complex fleets and platforms mission\-ready — managing the full maintenance, asset and service lifecycle at scale. We are reimagining how that software is built and delivered around AI, and we are investing in engineers who can put AI to work both inside our products and in how we build them.
You operate forward deployed — working shoulder\-to\-shoulder with A\&D customers in their own environments — to ship AI\-powered mobile capabilities that connect IFS to the systems that run their operations. This is a Principal\-level role: you are the person others build toward, setting technical direction across product groups, carrying deep expertise in Applied AI and Forward Deployed Engineering, and taking
end\-to\-end ownership with a you\-build\-it, you\-run\-it mindset — from design and development through testing, deployment and operational reliability in the field.
What you'll do
- Shape our next\-generation mobility solution. Help define and deliver the next\-generation mobile experience for aviation maintenance — a fast, reliable, field\-ready product that engineers trust to do their most critical work, including where connectivity is constrained.
- Build AI into the product. Design and ship AI\-powered product capabilities — model integration, prompt engineering, retrieval\-augmented generation (RAG), agentic architectures and the evaluation harnesses and MLOps that keep them reliable in production. You build AI systems, not just use them.
- Use AI to build the product. Treat AI\-assisted development and agentic tooling as core engineering capability. You raise the bar for how the team builds — measurably faster and better — and set the standard others adopt.
- Deploy forward. Embed with Aerospace \& Defense customers to deploy, integrate and harden solutions in their environment. You connect IFS to the systems that run their business — ERP, CRM, IoT, telemetry, and legacy and mission systems — and own the solution architecture in real customer context, under real\-world constraints.
- Own delivery end to end. Design, develop, test, deploy and operate across the full stack in modern general\-purpose languages. You own quality through automated testing, manage delivery through CI/CD, and ensure operational reliability in cloud, edge and mobile environments.
- Lead as a Principal. Drive technical strategy across product groups, make the architecture and shipping\-quality calls others defer, mentor engineers, and lift the AI and forward\-deployment practice of the wider organisation. Represent IFS credibly in front of customers and partners.
- Anchor to the customer. Ground technical decisions in direct customer evidence and business value. You understand the A\&D domain deeply enough to know which problems are worth solving and how AI changes the answer.
Qualifications
Essential
- Demonstrated, hands\-on experience leveraging AI for building products — using AI\-assisted and agentic development tooling as a core part of your workflow — and for building AI within products — shipping production AI capabilities using ML integration, prompt engineering, RAG, agentic architectures, evaluation and testing, and MLOps.
- Mobile engineering: a proven track record building and shipping production mobile applications, with the depth to set the technical direction for a mobile product. Native iOS development experience is strongly preferred given this role's focus on our next\-generation aviation\-maintenance mobility solution.
- Principal\-level impact: a proven track record as a recognised technical authority who drives strategy and delivery across multiple teams or product groups, with the depth to operate as a role model in the most complex, large\-scope work.
- Forward\-deployed strength: substantial experience deploying, integrating and operating enterprise software directly in customer environments, including integration with ERP, CRM, IoT and legacy systems, and owning solution architecture in customer context.
- Full\-lifecycle engineering: strong foundations across the delivery lifecycle — design and development, quality and testing, cloud and DevOps, and security — with proficiency in modern general\-purpose programming languages beyond any single domain framework.
- Leadership and ownership: end\-to\-end ownership with a you\-build\-it, you\-run\-it mindset, excellent communication with technical and non\-technical audiences, and a track record of growing the capability of the engineers and teams around you.
Nice to have
- Experience working in or with Aerospace \& Defense organisations, or on solutions for asset, maintenance, MRO or fleet\-readiness management.
- Experience with other enterprise asset management (EAM), field service or maintenance\-management solutions.
- Degree in Computer Science, Software Engineering or a related field, or equivalent practical experience.
- Experience of agile delivery and of working with globally distributed teams.
Additional Information
- 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
Salary Context
This $15K-$180K range is in the lower quartile 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 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($97K) sits 55% below the category median. Disclosed range: $15K to $180K.
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
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