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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
The Global Vice President of Commercial Execution for Loops owns the global commercial execution of the IFS Loops portfolio. Working to the sales plays and commercial strategy set with the SVP Industrial AI, this role is the execution arm of that strategy: it converts plays into field motion, runs the operating cadences that drive them, and is accountable for a reliable global Loops revenue forecast.
The role's centre of gravity is the scale engine — demand generation and volume execution through the Core sales organisation, and the larger-deal motion run at scale across regions. Alongside this, the GTM Lead works closely with the Strategic Seller – Global Loops to drive the most strategic pursuits, ensuring they are properly resourced, represented in the global forecast, and that the capability built in those deals flows back into the wider field.
This is an operational, commercially accountable, globally focused role. Success is measured on pipeline created, pipeline progressed, deals closed, forecast accuracy, and the growth of the larger-deal tier as a repeatable motion.
Key Responsibilities
Sales Play Execution & the Scale Engine
- Operationalise the Loops sales plays agreed with the SVP Industrial AI — launch them by region, sequence them across the fiscal year, and drive adoption to a measurable standard of use.
- Own the Core attach and standalone motions at volume: ensure every Core seller and AI seller knows the play in front of them, the target accounts, the qualification bar, and the next action.
- Build and run the larger-deal motion at scale — the tier above transactional landing. This is the role's primary growth lever.
- Instrument each play: entry criteria, expected conversion, cycle time, and the signals that say a play is working or needs to be pulled.
- Retire or rework plays that are not converting, and feed the evidence back into strategy with the SVP Industrial AI.
Operating Cadences
- Own the RVP cadence: a standing rhythm per region covering pipeline created, pipeline progressed, deals at risk, and committed close. Regional gaps are surfaced with a named recovery plan, not a status update.
- Own the seller-level cadence: direct working sessions with individual AEs and AI AEs on live opportunities — qualification, value case, competitive position, and close plan.
- Drive alignment of Core and AI seller activity where deals are jointly held, escalating comp, ownership, or access blockers to the SVP Industrial AI and regional leadership.
- Run a disciplined deal-inspection rhythm against the agreed qualification gates, keeping speculative or unqualified opportunities out of the forecast.
Demand Generation, Progression & Close
- Own global Loops pipeline creation to the agreed coverage multiple against the revenue target, by region and by motion.
- Partner with Marketing to convert plays into campaigns, and hold the loop closed from campaign to qualified opportunity to first meeting.
- Drive pipeline progression as a managed discipline — stage conversion, cycle time, and stalled-deal intervention.
- Provide hands-on deal shaping and closing support on larger pursuits: commercial construct, pricing and packaging, value case, and executive engagement.
- Improve win rate and reduce cycle time in the larger-deal tier as an explicit objective.
Strategic Deals — Working with the Strategic Seller
- Partner with the Strategic Seller – Global Loops to build and progress the portfolio's most strategic pursuits, bringing regional coverage, campaign support, and field mobilisation behind them.
- Apply the agreed strategic qualification criteria consistently, identifying and referring qualifying accounts into the strategic motion early rather than letting them land small.
- Represent strategic pursuits in the RVP and leadership cadences and carry them in the global forecast, with the deal narrative provided jointly.
- Absorb the capability downward: take the executive value frameworking, commercial constructs, and win/loss learning generated in strategic pursuits and embed them into the wider plays, so more of the field can run larger deals independently.
- Jointly manage the qualification threshold over time — as field capability rises, more pursuits move into the scale motion.
Forecast Ownership
- Own the global IFS Loops revenue forecast — accuracy, cadence, and the narrative behind it — across all motions including strategic pursuits.
- Maintain a defensible view of committed, in-call, and upside revenue by region and by motion, with the risk explicitly stated.
- Report commercial performance and required interventions to the SVP Industrial AI and senior leadership on a fixed rhythm.
- Track and manage the deal-mix target across strategic, larger-deal, and standalone-and-attach motions as a leading indicator of future predictability.
Enablement & Cross-Functional Execution
- Work with Sales Enablement to turn plays into usable field assets — playbooks, qualification guides, value calculators, objection handling — and to close the enablement gaps the cadences expose.
- Partner with Presales to ensure coverage and readiness are aligned to where the plays are being run, and to protect capacity for the largest pursuits.
- Feed structured field and customer evidence to Product Management to influence roadmap priority.
- Support Partner teams in extending the scale motions through the ecosystem where that adds reach or delivery capacity.
- Maintain a consistent global Loops narrative and competitive position in the field, in step with Marketing and Product Marketing.
Success Measures
- Delivery of the global Loops revenue number, with forecast accuracy at the agreed tolerance by quarter.
- Pipeline coverage sustained at the agreed multiple against target, by region and by motion.
- Measurable growth in the larger-deal tier — volume, win rate, and cycle time — as a repeatable motion rather than exceptions.
- Sales play adoption and conversion at the agreed standard across all regions, with underperforming regions on active, tracked recovery plans.
- Deal mix moving to the agreed balance across strategic, larger-deal, and standalone-and-attach motions.
- Pipeline hygiene: unqualified opportunities removed, stage conversion improving, stalled-deal intervention working.
- Core and AI seller engagement operating without escalation on ownership or access.
- Effective partnership with the Strategic Seller — qualifying accounts identified early, field mobilized behind strategic pursuits, capability transferred back into the scale plays.
What We’re Offering
- Salary Range: $200,000 - $215,000 annually + 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
Qualifications Qualifications
- 7+ years in enterprise software or SaaS across sales management, GTM execution, sales strategy, or business development, with direct accountability for a revenue number.
- Demonstrated ownership of a sales forecast at regional or global level, including the operating cadence and inspection discipline behind it.
- Proven track record running operating cadences with sales leadership and individual sellers — pipeline reviews, deal inspection, recovery planning.
- Experience building and scaling a repeatable mid-to-large deal motion, not solely enterprise strategic pursuits.
- Hands-on deal shaping and closing experience: commercial construct, pricing and packaging, value case, executive engagement.
- Experience driving demand generation and pipeline creation in partnership with Marketing, with accountability for qualified output.
- Track record influencing a global, matrixed sales organisation without direct authority, including alignment of core and specialist sellers.
- Strong understanding of AI-driven enterprise software — agentic platforms, optimisation, simulation, decision intelligence, or autonomous operations.
Skills & Competencies
- Operator first: comfortable in the weekly detail of pipeline, deals, and forecast, with the judgement to know which deals to touch.
- Strong commercial and revenue acumen; fluent in deal construct, pricing mechanics, and value selling.
- Ability to execute an agreed strategy faithfully and improve it with evidence — this is an execution role, not a strategy role.
- Exceptional stakeholder management; credible with regional sales leaders, individual sellers, and executives alike.
- Data-driven: converts pipeline and conversion data into specific, named interventions.
- Strong programme and cadence discipline — makes rhythms stick across time zones and competing priorities.
- Direct and constructive under pressure; able to hold regions to account without breaking the relationship.
- Committed to building scalable, repeatable motions rather than closing deals personally.
Additional Information 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 $200K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Disclosed range: $200K to $215K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
IFS AI Hiring
IFS has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Melbourne, Victoria, Australia, Itasca, IL, US, London, England, UK. Compensation range: $215K - $215K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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