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About This Role
IBP is seeking an Enterprise Data \& AI Strategy Manager to accelerate our digital evolution. Reporting to the VP of Internal Audit, this role serves as a strategic connector—aligning business units, PMO, IT, and leadership to deliver scalable data solutions, AI‑enabled insights, and enterprise automation.
You’ll play a pivotal role in modernizing our data environment and operationalizing AI across a decentralized organization.
Key responsibilities:
Build strong relationships with cross\-functional partners, regularly communicating progress, insights, and alignment between data strategies and business goals
Ability to operate in a highly decentralized environment
Partner with IT, data stewards, and business unit leaders to define evolving data requirements
Enforce data governance frameworks, standards, and policies to ensure consistency, compliance, and data integrity
Monitor and promote data integrity across systems
Support data remediation by leveraging AI\-driven tools for gap\-filling, correcting, matching, and auditing data, ensuring data quality and consistency across systems
Preferred Experience \& Qualifications
8\+ years of experience in data, analytics, and enterprise transformation roles
Demonstrable experience in creating \& modernizing enterprise data reporting frameworks \& supporting departments
Experience working on hyperscalers (Azure, AWS) and with cloud data warehousing platforms (Fabric, Databricks, Snowflake etc.) Experience transitioning from a fragmented legacy data environment to cloud\-based medallion architecture
Experience with ERP, AP, and CRM systems (such as Sage 100, QuickBooks, Acumatica, MuleSoft, Salesforce)
Experience with data governance frameworks
Familiarity with Purview or Unity Catalog a plus
Create and scale intelligent autonomous agents that provide value\-add, goal\-driven automation experiences
Enable and execute multi\-agent workflows across systems, enhancing decision\-making and workflow adaptability
Strong preference for successful AI rollouts to production
Support data remediation by leveraging AI\-driven tools for gap\-filling, correcting, matching, and auditing data, ensuring data quality and consistency across systems.
Required Skills:
Analytical \& problem\-solving abilities \- strong analytical skills to identify trends, solve complex issues, and translate insights into actionable strategies
Communication skills – strong verbal and written communication skills, with the ability to clearly convey complex data concepts to non\-technical audiences
Relationship building \& business engagement – strong interpersonal skills to effectively collaborate with cross\-functional teams, influence decision\-making, and drive alignment/adoption of solutions across a decentralized branch network
Data Governance development \& adoption \- including:
Knowledge of data governance frameworks, data quality management, and compliance practices to ensure data integrity and security
Implement data standards practices and enforcement
Establish clear data classification and access controls
Lead overall data stewardship including defining roles and responsibilities across the organization
Ensure traceability, transparency, and consistency of enterprise data outputs
Enterprise Data Platform technical proficiency – including:
Own the Enterprise Data Roadmap, order and prioritization, dependencies, and timelines easily accessible by stakeholders
Create \& implement framework to prioritize ingestion, transformation, and reporting of data sources
Retire legacy systems and data transformation workflows and ensure complete and accurate transition to new data environment
Oversee testing and QA
Create and monitor the automation of alerts, logs, and Lakehouse availability reporting
AI Strategy \& Execution proficiency – including:
Systems\-based thinking and focus on value creation
Establish Enterprise AI frameworks, approach, and guardrails
Translate enterprise AI strategy into executable use cases and initiatives
Assist leadership to prioritize AI investments and manage delivery
Partner with IT in testing and exploration in sandbox environments
Evaluate and mitigate risks associated with the use of ‘Shadow AI’ across the Enterprise
Schedule: Hybrid
Compensation: $115k–$140k base \+ manager bonus
Physical Demands:
Reasonable accommodations may be made to enable individuals with disabilities to perform essential job functions. The ability to lift light to moderate weight and engage in activities such as sitting for extended periods to complete computer tasks, as well as occasional lifting, standing, bending, or reaching, is required.
Why we love it here
At IBP, we invest in all of our employees. That means robust benefits (medical, dental, vision, short\- and long\-term disability, accident, and critical illness, company\-paid life, and retirement plans), paid time off, and the IBP Foundation, which offers scholarships, emergency assistance, and volunteer matching for everyone in the IBP family. We pride ourselves on supporting our teammates’ quest for additional training and certifications outside of what we provide by paying for those opportunities.
EEO Statement
We are an Equal Opportunity Employer and consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veterns.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $115K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Installed Building Products, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($127K) sits 42% below the category median. Disclosed range: $115K to $140K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Installed Building Products AI Hiring
Installed Building Products has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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