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About This Role
What's in it for you?
Thanks to our commitment to creating a supportive and engaged workplace culture, Gorilla Glue has been recognized as a top place to work for 16 years in a row. We offer competitive compensation packages, comprehensive healthcare benefits, and other perks and incentives to ensure our employees feel valued and supported.
Joining the Gorilla Glue family means being part of a team passionate about building strong brands and promoting a culture of excellence. As an employee, you'll be able to help create high-quality products and build brands that people love. Join us in an inspiring workplace where you can belong, grow, and make a difference!
Summary Description The Microsoft Platform & AI Engineer supports the implementation, administration, and continuous improvement of The Gorilla Glue Company's Microsoft 365, Power Platform, Copilot Studio, and AI-enabled solutions that improve productivity, automate business processes and deliver measurable business value. This role is suited to a hands-on Microsoft engineer who combines strong Microsoft 365 administration with practical experience building solutions in Copilot Studio and Power Platform. The position will translate approved business needs into secure, supportable agents, applications, workflows, and platform improvements. The successful candidate will work closely with business functions to identify and develop automation opportunities, deliver Copilot Studio agents and Power Platform solutions. They will collaborate with Enterprise Data Management, Infrastructure, Security, Application Services, Service Desk, and business stakeholders to deliver solutions in line with enterprise standards.
The role operates under the direction of the Manager - Global MS Platforms and is focused on the implementation, integration, support, and continuous improvement of Microsoft platform and AI solutions
What will your day look like?Microsoft 365 Administration & Platform Engineering* Configure and support Microsoft 365, Microsoft 365 Copilot, Copilot Studio, and Power Platform capabilities to enable business productivity, automation, and AI-driven experiences.
- implement integrations between Copilot Studio, Power Platform, SharePoint Online, Teams, OneDrive, Exchange Online, and other approved Microsoft technologies.
- Develop secure workflows, actions, connectors, and knowledge experiences that enable agents and AI solutions to interact effectively with Microsoft 365 services and business data.
- Troubleshoot and resolve issues relating to Copilot, Copilot Studio, Power Platform, Microsoft 365 integrations, permissions, data access, and platform services.
Copilot Studio & AI Solution Development* Design, build, test, deploy, and maintain Copilot Studio agents that address approved business needs.
- Configure topics, generative orchestration, knowledge sources, actions, authentication, variables, and conversation flows to create effective agent experiences.
- Integrate agents with Power Automate, Microsoft 365, Dataverse, approved APIs, Microsoft Graph and other enterprise systems.
- Apply responsible AI, security, data protection, access control, and governance requirements throughout the solution lifecycle.
- Perform testing, troubleshooting, monitoring, and continuous improvement of agents based on usage, quality, risk, and stakeholder feedback.
- Develop reusable patterns, templates, and components aligned to approved architecture standards.
- Maintain awareness of emerging AI technologies, architectures, and best practices, and apply that knowledge to the design, implementation, and continuous improvement of enterprise AI solutions.
Power Platform Solution Development* Design and build business solutions using Power Apps, Power Automate, Dataverse, and other approved Power Platform capabilities.
- Automate business processes, approvals, notifications, data capture, and integrations using secure and maintainable workflows.
- Use solutions, connection references, environment variables, and appropriate application lifecycle management practices to move work through development, test, and production environments.
- Configure roles, permissions, connectors, and data access in accordance with governance.
- Support existing applications and automations by diagnosing failures, managing changes, and improving reliability and usability.
Copilot Deployment, Adoption & Enablement* Support Microsoft 365 Copilot deployment, readiness, onboarding, and adoption activities.
- Deliver demonstrations, training sessions, office hours, and practical guidance for employees and business teams.
- Create user guidance, solution documentation, FAQs, learning materials, and reusable prompting examples.
AI Use-Case Discovery & Business Partnership* Identify opportunities for AI, agents, automation, and Microsoft platform improvements.
- Provide technical input into use-case assessments and support approved use cases through design and implementation.
- Develop proof of concepts and production-ready solutions for approved initiatives.
- Communicate technical concepts clearly to business stakeholders and provide practical recommendations and progress updates.
Governance, Security & Operational Support* Implement and support governance standards for Microsoft 365, Power Platform, Copilot Studio, and enterprise AI solutions.
- Ensure solutions align with security, privacy, compliance, data governance, accessibility, and supportability requirements.
- Participate in design reviews, testing, release planning, change control, incident resolution, and post-implementation reviews.
- Identify technical risks, platform constraints, licensing considerations, and support requirements, escalating decisions where appropriate.
- Collaborate with IT teams and approved partners to resolve platform, integration, identity, data, or service issues.
Project Delivery & Implementation* Deliver assigned Microsoft platform and AI initiatives under the direction of the Managed – Global MS Platforms.
- Own assigned project tasks and technical workstreams from discovery through implementation and transition to support.
- Maintain clear plans, actions, risks, dependencies, testing evidence, and status updates.
- Coordinate with users, technical teams, vendors, and stakeholders to support successful implementation and adoption.
- Contribute to continuous improvement of delivery methods, standards, documentation, and reusable solution patterns.
What will you bring to the table?
Education:* Bachelor’s degree in Information Technology, Computer Science, Business, or a related discipline; equivalent experience may be considered.
Required* Minimum 3 years professional work experience
- Demonstrated experience designing, building, testing, and deploying solutions in Microsoft Copilot Studio.
- Strong practical delivery with Power Platform, particularly Power Apps, Power Automate, and Dataverse.
- Experience integrating Copilot Studio, Power Platform, Microsoft 365 Services, API’s, Connectors, SharePoint, Dataverse.
- Experience designing AI-enabled solutions, workflows, automations, and business process improvements using Microsoft technologies.
- Working knowledge of environments, security roles, permissions, data loss prevention, solution management, and application lifecycle practices within Power Platform.
- Experience gathering business requirements and translating them into technical solutions, prototypes, or production implementations.
- Ability to troubleshoot agents, applications, workflows, integrations, identity, permissions, and data-related issues.
- Strong written and verbal communication skills, including the ability to train users and explain technical concepts to non-technical audiences.
- Demonstrate ownership, organization, documentation, and problem-solving skills.
- Commitment to responsible AI, security, privacy, governance, and continuous learning.
Preferred* Experience with SharePoint Online, Teams, Exchange Online, Entra ID, Microsoft Graph, or PowerShell.
- Experience using Azure services or Azure AI capabilities in support of Microsoft platform solutions.
- Experience with Power BI, Power Pages, AI Builder, custom connectors, or Dynamics 365.
- Experience supporting a multinational or distributed organization.
- Relevant Microsoft certifications such as PL-200, PL-400, MS-102, or AI-900.
Company Values Adhere to The Gorilla Glue Company’s Core Values that ensure everyone works together to exceed expectations:* Have fun, be passionate and show it.
- Act with integrity and respect in all relationships.
- Communicate openly and appreciate the effort of others.
- Embrace knowledge, growth, and change.
- Strive for excellence in what we do every day.
Are you ready to join one of Cincinnati’s Top Workplaces? Our employees love working here because of our environment, which is driven by our company mission and core values. If you have the right qualifications and thrive in a fun and fast-paced environment, we have a great opportunity for you to grow with us!
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Gorilla Glue, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Gorilla Glue AI Hiring
Gorilla Glue has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sharonville, OH, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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