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About This Role
Creating Peace of Mind by Pioneering Safety and Security
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*At Allegion, we help keep the people you know and love safe and secure where they live, work and visit. With more than 40 brands, 14,000\+ employees globally and products sold in 130 countries, we specialize in security around the doorway and beyond.*
*Additionally, Allegion is proud to be recognized with the 2026 Gallup Exceptional Workplace Award (GEWA) for the third consecutive year, earning distinction in both the employee engagement and strengths categories. This year, Allegion also received Gallup’s With Distinction honor — a designation reserved for a select group of organizations that go above and beyond in building exceptional workplace cultures.*
Job Title
The AI Solutions Engineer is responsible for designing, building, and evaluating managed AI agents that automate and augment work across Allegion. Embedded with teams throughout the business, this role learns their processes firsthand, translates needs into clear requirements and success criteria, and stands up agents that produce consistent, trustworthy results with the right level of human oversight. The role also builds the reusable patterns, templates, and evaluation practices that make each successive agent faster and safer to deliver.
*Qualified candidates must be legally authorized to be employed in the United States. The company does not intend to provide sponsorship for employment visa status (e.g., H\-1B, TN, etc.) for this employment position.*
At Allegion, we are driven by a bold vision: redefining safety while empowering our employees to thrive. When you join our team, you become part of a culture that values innovation, purpose, and excellence. This role offers the benefits of our dynamic hybrid work model—combining in\-person collaboration for meaningful moments with the flexibility of remote work. Since hybrid arrangements can vary based on the needs of the individual, team and business, your talent acquisition partner will provide specific hybrid details about this role.
We are committed to fostering a healthy work\-life balance and building meaningful connections, ensuring you have the tools, resources, and support needed to excel in any environment. Together, we’ll unlock your potential and create a lasting impact.
*While this is the current structure and we currently have no plans to change, we reserve the right to make changes to the hybrid schedule as needed at the Company’s discretion.*
What You Will Do:
- Embedding with teams across Allegion to learn their processes firsthand and identify where an agent will create measurable value.
- Translating unfamiliar business processes into clear agent requirements, scope, and success criteria.
- Designing and building agents that are safe, reliable, and produce consistent outputs.
- Defining what "good" means for each use case and building evaluations that measure accuracy, consistency, and regression over time.
- Deciding where human oversight is warranted \- particularly for client\-facing outputs or writes to systems of record \- and designing those checkpoints in.
- Building reusable patterns, skills, templates, and best practices that standardize agent development across the company.
- Piloting agents with business users and leading acceptance testing before rollout.
- Partnering with data scientists, business partners, and peer AI teams to prioritize use cases and align on delivery.
- Applying responsible\-AI practices and staying current with advances in agentic AI and evaluation methods.
What You Need to Succeed:
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
- 3\+ years total experience delivering AI, software, or data solutions, including hands\-on work building LLM\-based applications or agents.
- Experience designing and building AI agents, including tool use, external system and data integration, and multi\-step workflow orchestration.
- Strong proficiency with generative AI and large language models, including prompt engineering for reliable, high\-quality outputs.
- Experience defining and implementing evaluations for AI systems that measure quality, consistency, and regression over time.
- Sound judgment on human\-in\-the\-loop design \- knowing when oversight is warranted and designing effective review checkpoints.
- Requirements\-discovery and facilitation skills \- able to embed with a business team, learn an unfamiliar process quickly, and translate it into clear requirements.
- Strong Python proficiency and hands\-on experience with cloud platforms; Azure preferred, AWS or GCP acceptable.
- Excellent communication skills, with a proven track record of working effectively with both technical teams and non\-technical stakeholders.
- Comfortable establishing new standards and patterns in ambiguous, greenfield territory and refining them with partner teams.
- Ability to work independently and collaborate effectively, in person and remotely.
- Analytical approach to problem\-solving, sense of urgency, ability to manage multiple initiatives, and self\-direction in keeping current with rapidly changing technology.
- Experience with the Model Context Protocol (MCP) or similar frameworks, and with integrating systems of record or CRM platforms (e.g., Microsoft Dynamics), is a plus.
- Experience in a customer\-facing, consulting, solutions\-engineering, or forward\-deployed role is a plus.
- Background in data science, machine learning, or applied statistics, particularly in evaluation or measurement methodology, is a plus.
Why Work for Us?
Allegion is a Great Place to Grow your Career if:
- You're seeking a rewarding opportunity that allows you to truly help others. With thousands of employees and customers around the world, there’s plenty of room to make an impact. As our values state, “this is your business, run with it”.
- You’re looking for a company that will invest in your professional development. As we grow, we want you to grow with us.
- You want a culture that promotes work\-life balance. Our employees enjoy generous paid time off, because at Allegion we recognize that you have a full life outside of work!
- You want to work for an award\-winning company that invests in its people. Allegion is proud to be a recipient of the Gallup Exceptional Workplace Award for the second year in a row, recognizing our commitment to employee engagement, strengths\-based development, and unlocking human potential.
What You’ll Get from Us:
- Health, dental and vision insurance coverage, helping you “be safe, be healthy”
- Unlimited Paid Time Off
- A commitment to your future with a 401K plan, which currently offers a 6% company match and no vesting period
- Health Savings Accounts – Tax\-advantaged savings account used for healthcare expenses
- Flexible Spending Accounts – Tax\-advantaged spending accounts for healthcare and/or dependent daycare expenses
- Disability Insurance –Short\-Term and Long\-Term coverage, paid for by Allegion, provides income replacement for illness or injury
- Life Insurance – Term life coverage with the option to purchase supplemental coverage
- Tuition Reimbursement
- Voluntary Wellness Program – Simply complete wellness activities and earn up to $2,000 in rewards
- Employee Discounts through *Perks at Work*
- Community involvement and opportunities to give back so you can “serve others, not yourself”
- Opportunities to leverage your unique strengths through CliftonStrengths assessment \& coaching
Apply Today!
Join our team of experts today and help us make tomorrow’s world a safer place!
*Not sure if your experience perfectly aligns with the role?* *Studies have shown that some people are less likely to apply to jobs unless they meet every single qualification* *and* *every single preferred qualification of a job posting. At Allegion, we are dedicated to building a diverse, inclusive, and authentic workplace. So, if you’re excited about this role but your past experience doesn’t align perfectly with every item in the job description, we encourage you to apply anyway. You may be just the right candidate for this role*
We Celebrate Who We Are!
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Allegion is committed to building and maintaining a diverse and inclusive workplace. Together, we embrace all differences and similarities among colleagues, as well as the differences and similarities within the relationships that we foster with customers, suppliers and the communities where we live and work. Whatever your background, experience, race, color, national origin, religion, age, gender, gender identity, disability status, sexual orientation, protected veteran status, or any other characteristic protected by law, we will make sure that you have every opportunity to impress us in your application and the opportunity to give your best at work, not because we’re required to, but because it’s the right thing to do. We are also committed to providing accommodations for persons with disabilities. If for any reason you cannot apply through our career site and require an accommodation or assistance, please contact our Talent Acquisition Team.
© Allegion plc, 2023 \| Block D, Iveagh Court, Harcourt Road, Dublin 2, Co. Dublin, Ireland
REGISTERED IN IRELAND WITH LIMITED LIABILITY REGISTERED NUMBER 527370
Allegion is an equal opportunity and affirmative action employer
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 Allegion, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Allegion AI Hiring
Allegion has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Carmel, IN, US.
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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