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Join a Team That Is Building What Comes Next
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Titan International is looking for a Transformation Analyst to support the Chief Transformation Officer office as we advance enterprise transformation, practical AI adoption, strategic initiative governance, and disciplined execution across the business.
This is a high\-visibility role for someone who is curious, organized, analytical, and comfortable turning complex work into clear plans, useful insights, and executive\-ready materials. The right person will help connect strategy, data, technology, project activity, and leadership reporting so Titan’s transformation work stays focused, visible, and tied to measurable business value.About Titan
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Titan International, Inc. is a leading global manufacturer of off\-highway wheels, tires, assemblies, and undercarriage products serving agriculture, construction, earthmoving, mining, forestry, and related markets. Our work supports customers who rely on durable products, practical innovation, quality service, and strong execution in demanding environments.What You’ll Do
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- Support Titan’s enterprise transformation and AI roadmap by helping define business problems, use cases, success measures, risks, dependencies, and implementation considerations.
- Research AI tools, platforms, vendors, and emerging capabilities that may support Titan’s manufacturing, operations, supply chain, finance, legal/compliance, HR, commercial, engineering, and enterprise systems teams.
- Administer and improve the company’s strategic planning management platform, including initiative records, milestones, dashboards, scorecards, ownership, dependencies, risks, budgets, and executive reporting views.
- Track transformation projects and help keep deliverables, decisions, timelines, follow\-ups, risks, and next steps organized and visible.
- Prepare clear, polished materials for senior leaders, including briefing documents, decision summaries, project updates, board\-ready content, talking points, presentations, and communication drafts.
- Coordinate with internal stakeholders and external partners, including consultants, AI technology providers, and strategic planning platform providers.
- Conduct structured discovery with business teams to understand workflows, pain points, data gaps, manual processes, and opportunities for improvement.
- Support business case development, ROI summaries, value hypotheses, initiative charters, process maps, requirements summaries, and implementation plans.
- Help develop practical education and change enablement materials that support responsible AI experimentation, adoption, and capability building across the organization.
What You Bring
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- Bachelor’s degree in business, engineering, computer science, data science, information systems, finance, operations, or a related field required.
- Strong analytical, critical\-thinking, and problem\-structuring skills.
- Experience or demonstrated interest in AI, automation, analytics, digital transformation, enterprise systems, or manufacturing technology.
- Familiarity with project management, program management, agile methods, initiative tracking, or strategic planning tools.
- Excellent written and verbal communication skills, including the ability to summarize complex information for executive audiences.
- Strong organizational discipline and the ability to manage multiple initiatives, owners, timelines, risks, and follow\-ups.
- Ability to work independently in ambiguous, fast\-moving, and evolving environments.
- Professional judgment, discretion, confidentiality, and sound decision\-making.
- Proficiency with Microsoft Office tools, including Word, Excel, PowerPoint, Teams, SharePoint, and Copilot or related AI\-enabled productivity tools.
Experience That Will Help You Stand Out
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- Experience preparing dashboards, status reports, business cases, meeting summaries, leadership briefings, or executive presentations.
- Comfort working with data, process maps, workflow documentation, requirements gathering, and cross\-functional business processes.
- Interest in manufacturing, industrial operations, product development, supply chain, or enterprise transformation.
Who Will Thrive in This Role
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You will do well in this role if you are strategic but practical, intellectually curious, highly organized, and able to create clarity where work is complex or still forming. You should be comfortable asking thoughtful questions, researching unfamiliar topics, connecting technology ideas to business outcomes, and following through on the details that help leaders make better decisions.Why This Role Matters
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This role sits at the center of Titan’s transformation agenda. The work is practical, visible, and connected to initiatives that are intended to simplify processes, strengthen accountability, improve speed, connect data, reduce risk, and create measurable value. The Transformation Analyst will help shape how transformation work is organized, communicated, governed, and scaled across the organization.Compensation and Benefits
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Titan supports the health, financial well\-being, and career development of our employees. The salary on this role is estimated to begin at $80,000 depending on experience. U.S. benefits may include medical and dental coverage, employer\-matched 401(k), life insurance, short\- and long\-term disability insurance, vacation, tuition reimbursement, career advancement opportunities, employee discounts, wellness programs, disability insurance, vision coverage, and Titan University.Equal Opportunity Employer
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Titan International, Inc. is an Equal Opportunity Employer and maintains a drug\-free workplace.
Salary Context
This $65K-$98K 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 The Carlstar Group, 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 ($82K) sits 62% below the category median. Disclosed range: $65K to $98K.
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.
The Carlstar Group AI Hiring
The Carlstar Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Franklin, TN, US. Compensation range: $98K - $98K.
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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