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About This Role
About The Heritage Group
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The Heritage Group is a fourth\-generation, family\-owned business focused on construction and materials, environmental services and specialty chemicals. Over the last 90\+ years, the Heritage portfolio has grown to include more than 50 companies that employ more than 6,000 people. What unites this diverse group of businesses and individuals is our commitment to create a safer, more enriching, and sustainable world by harnessing the power of family.
The AI Administrator is the enterprise owner for approved AI platforms and services. Operating within the ITSS Digital Productivity \& Collaboration team, this role administers and governs Microsoft 365 Copilot, Copilot Studio, Azure AI, Microsoft Fabric AI capabilities, and other approved LLM platforms such as Claude. The role ensures AI services are secure, cost\-efficient, compliant, and aligned with identity governance standards. Like other platform and identity administration roles, this position emphasizes platform reliability, licensing and cost optimization, governance enforcement, and risk management rather than business solution development.
\*This role is hybrid, based out of our Indianapolis, IN offices. Qualified candidates must currently reside within 75 miles of the Indianapolis, IN area\*
Essential Functions
- Enterprise AI platform governance and administration: Own tenant\-level administration for approved AI platforms, including Microsoft 365 Copilot, Copilot Studio agent environments, Azure AI services, and integrated generative AI tools. Configure environments, roles, controls, and access models; define and enforce usage standards, security controls, and compliance requirements in partnership with Security and Compliance.
- Licensing and consumption\-based billing management: Oversee AI licensing, capacity, and cost governance. Manage license procurement, assignment, and de\-provisioning; administer pay\-as\-you\-go billing and Azure consumption plans; monitor usage, budgets, and alerts; support business\-unit chargebacks; and serve as the primary liaison with the Cloud Solution Provider for licensing, renewals, invoicing, and model optimization.
- Monitoring and continuous platform oversight: Track AI platform performance, adoption, utilization, capacity, feature releases, and policy changes. Review Microsoft updates, adjust configurations as needed, identify optimization opportunities, and communicate service impacts or access changes to users and support teams.
- Identity and security integration: Treat AI services, agents, bots, service principals, and related non\-human accounts as governed identities. Partner with Identity Management and Security to manage lifecycle controls, access reviews, credentials, role assignments, privacy safeguards, and risk mitigation for AI services.
- AI adoption and governed experimentation: Lead enterprise AI adoption efforts by partnering with business units, IT, Security, Compliance, Data, and Finance to encourage practical experimentation with approved AI tools. Establish clear guardrails, intake paths, usage guidance, and support models that allow teams to explore AI capabilities safely while maintaining appropriate controls for data protection, identity, compliance, cost, and risk.
- Operational support and cross\-functional collaboration: Serve as the subject matter expert for enterprise AI platforms. Provide advanced support and guidance to IT teams and business units, help solution teams stay within governance guardrails, coordinate with data, cloud, security, finance, and business stakeholders, and support AI enablement and literacy efforts.
- *Additional duties and responsibilities as assigned, including but not limited to continuously growing in alignment with the Company’s core values, competencies, and skills.*
Education Qualifications
- Required Bachelor's Degree in Information Systems, Computer Science, or a related field, or equivalent work experience.
Experience Qualifications
- Required 5\+ years of experience in enterprise IT administration, cloud platform management, IT operations, Microsoft 365 administration, or Azure services.
- Preferred prior experience in AI administration
- Strongly preferred working knowledge of Microsoft’s AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI, and tenant\-level policies and settings
- Strongly preferred experience and familiarity with consumption\-based billing, Azure cost management, Microsoft 365 Admin Center, Azure portal, PowerShell or scripting, data privacy, compliance, ethical AI practices, and governed administration of alternate LLM platforms.
- Strong understanding of identity governance, access controls, permission design, usage policies, risk management, change control, and compliance requirements for rapidly evolving technology platforms.
- Ability to work cross\-functionally, influence without direct authority, explain technical and cost concepts in business terms, partner with vendors and finance teams, and operate effectively in a fast\-changing AI environment.
Skills and Abilities
- Professional demeanor and team player with the ability to establish and maintain cooperative and effective working relationships
- Outstanding customer service/communication skills
- Strong verbal/written communication \& data presentation skills, including an ability to effectively communicate with both business and technical teams
- Desire to learn and acquire expertise in new technologies
- Microsoft Office Suite expertise: Outlook, Excel, Word, etc
Working Conditions/Physical Demands
- Work is primarily performed in an office or professional setting using standard business equipment; hybrid or remote work may be available based on role and business needs.
- Work is generally performed during standard business hours; occasional extended hours may be required to meet business or project demands.
- Minimal travel expected; occasional travel for meetings, training, or company events may be required.
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.
\#TheHeritageGroup
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 The Heritage 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 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.
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.
The Heritage Group AI Hiring
The Heritage Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US.
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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