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About This Role
Location: Chicago, IL (Relocation assistance available for candidates open to moving to Chicago.)
Why project44?
At project44, we believe in better.
We challenge the status quo because we know a better supply chain isn’t just possible, it’s essential. Better for our customers. Better for their business. Better for the world.
With our Decision Intelligence Platform, Movement, we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward.
Headquartered in Chicago, IL, with a 2nd HQ in Bengaluru, India, we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose. If you’re driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high-performance team – we should talk.
What You’ll Do:
The Vice President of AI Operations and GIS will own the global IT and enterprise applications strategy, combining strategic leadership with hands-on execution. This role is accountable for critical platforms including NetSuite (ERP), Salesforce (CRM), and UKG (HRIS), as well as IT infrastructure, cybersecurity alignment, and the broader enterprise applications portfolio.
As a PE-backed SaaS company scaling rapidly, this leader will partner with Finance, HR, Operations, Legal, and Engineering to modernize core processes, integrate and automate systems, and ensure security, compliance, and reliability at scale. The mandate is to deliver measurable business outcomes while preparing the company for IPO-readiness and future growth.
This role will also build and develop a high-performing EBS team, blending a player-coach mindset with the ability to scale and empower talent, while fostering a culture of accountability, service excellence, and continuous improvement.
What You’ll Do
IT & Business Systems Leadership
- Define and execute a global IT and enterprise applications roadmap aligned with business growth.
- Oversee NetSuite, Salesforce, UKG, collaboration platforms, and core IT infrastructure.
- Ensure systems are secure, scalable, and designed for a rapidly expanding SaaS business.
- Establish policies, governance, and best practices for IT and enterprise applications.
- Ensure systems’ availability, security, and scalability to support a global workforce.
- Establish IT policies, standards, and best practices to ensure operational excellence and compliance
NetSuite, Salesforce & UKG Ownership
- Serve as senior owner of NetSuite ERP, Salesforce CRM, and UKG HRIS.
- Drive enhancements, integrations, and automation across Finance, HR, Operations, and go-to-market processes.
- Partner with all business leaders to streamline quote-to-cash, billing, HR, supply chain, and reporting workflows.
- Assist with Data ownership, best practices with a mindset for accuracy and growth
Strategic Growth & AI Initiatives
- Lead digital transformation efforts across ERP, CRM, HRIS, and connected SaaS platforms.
- Apply AI and automation within enterprise systems to streamline processes, improve data quality, and enable operational efficiency, while partnering with the AI Ops team on broader cross-functional initiatives.
- Partner with Operations to modernize end-to-end processes that support global scale and rapid growth.
Team & Vendor Leadership
- Build, mentor, and inspire a high-performing IT & applications team.
- Operate with a player-coach mentality: willing to roll up your sleeves while developing talent.
- Manage external partners, MSPs, and system integrators effectively.
- Own IT and applications budgets with a focus on ROI.
- Lead vendor and consulting partner relationships as well as an optimal global systems budget/forecast supporting all applications
- Lead, mentor, and grow a team of IT professionals and enterprise application specialists.
- Foster a culture of service excellence, accountability, and continuous improvement.
Strategic Initiatives
- Support IT and enterprise applications strategy in alignment with Project44’s business objectives.
- Drive modernization initiatives, including automation, integration, and cloud adoption.
- Ensure compliance with relevant frameworks (e.g., IPO readiness, GDPR, ISO).
What You’ll Need
- 12+ years of progressive IT and enterprise systems leadership, with at least 5+ years in senior roles.
- Proven expertise with NetSuite ERP, Salesforce CRM, and UKG HRIS, including integration and process optimization.
- SaaS / subscription business model experience required; private equity–backed company experience preferred.
- Demonstrated success scaling IT and enterprise systems in a global, fast-growth company.
- Strong background in IT infrastructure, cybersecurity, enterprise SaaS, and governance.
- Experience enabling automation, data quality, and process optimization across enterprise systems.
- Excellent leadership, communication, and cross-functional partnership skills.
- Bachelor’s degree in IT, Computer Science, or related field (Master’s preferred).
- + In-office Commitment:Our office is where ideas spark, connections thrive, and innovation comes alive. We are looking for candidates who are enthusiastic and committed to joining our team on-site, in our beautiful headquarters four days a week. Together, we’re building something extraordinary—learn, grow, and thrive in our fast-paced, transformative environment. + Relocation offered: We’re proud to call Chicago home - a walkable downtown, amazing food, and a team that makes big ideas move fast. And we’re ready to help you make it yours, project44 offers relocation assistance for team members joining us from outside the area.
Why You’ll Love Working Here
- High-growth SaaS company backed by world-class investors.
- Opportunity to lead transformation at the intersection of IT, enterprise systems, Operations, and AI.
- You’ll inherit a modern systems foundation already running NetSuite, Salesforce, UKG, and Retool with Workato-based automation.
- Competitive compensation, equity, and benefits.
- Inclusive, value-driven culture where diverse voices and experiences are celebrated.
Diversity & Inclusion:
At project44, we're designing the future of how the world moves and is connected through trade and global supply chains. As we work to deliver a truly world-class product and experience, we are also intentionally building teams that reflect the unique communities we serve. We’re focused on creating a company where all team members can bring their authentic selves to work every day.
We’re building a company that every one of us at project44 is proud to work for, and our journey of becoming a more diverse, equitable and inclusive organization, where all have a sense of belonging, is shaped through the actions of our leadership, global teams and individual team members. We are resolute in our belief that each team member has an equal responsibility to mold and uphold our culture.
project44 is an equal opportunity employer seeking to enrich our work environment by creating opportunities for individuals of all backgrounds and experiences to thrive. If you share our values and our passion for helping the way the world moves, we’d love to review your application!
For any accommodation needed during the hiring process, please email [email protected]. Even if you don’t meet 100% of the above job description you should still seriously consider applying. Studies show that you can still be considered for a role if you meet just 50% of the role’s requirements.
#LI-Hybrid
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Convey, 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 $212,040 based on 13,780 positions with disclosed compensation.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Convey AI Hiring
Convey has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $193,440 across 450 tracked positions. That's 9% below the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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