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About This Role
About Us:
A Fortune 500® company, EMCOR Group, Inc. (NYSE: EME) is a leader in mechanical and electrical construction, industrial and energy infrastructure, and building services. EMCOR companies plan, install, operate, maintain, and protect the sophisticated and dynamic systems that create facility environments—such as electrical, mechanical, lighting, air conditioning, heating, security, fire protection, and power generation systems—in virtually every sector of the economy and for a diverse range of businesses, organizations, and government.
Job Summary:
EMCOR Group, Inc. seeks an AI Security Engineer who will be responsible for securing the organization’s artificial intelligence capabilities—including GenAI systems, ML pipelines, model hosting, AI integrations, and AI\-enabled business processes—through a combination of security architecture, governance, risk management, and hands\-on engineering. This role designs and implements security controls to reduce risks such as prompt injection, data leakage, model exfiltration, supply\-chain compromise, unauthorized use, and AI\-driven abuse, while enabling safe adoption across the enterprise.
Essential Duties \& Responsibilities:
- Adopt and maintain the Enterprise AI Security Reference Architecture
- Establish security patterns and guardrails for AI use cases including approved data sources, allowed tools, etc.
- Conduct threat modeling for AI systems and integrations and translate threats into technical controls
- Operationalize AI governance with EMCOR Legal and Risk
- Work with EMCOR Security Governance, Risk and Compliance (GRC) to maintain the AI risk assessment process
- Implement and maintain controls to prevent data leakage and sensitive information exposure in AI prompts, outputs, logs, and training sets
- Work with EMCOR Security Identity and Access Management (IAM) and GRC to deploy and tune protections like DLP, data classification, prompt and response filtering, RAG hardening and model endpoint security
- Integrate and maintain AI security testing capabilities such as prompt injection, model behavior evaluations, adversarial testing, etc.
- Work with EMCOR Security Operations to define and implement logging and monitoring requirements, create detection use cases, and playbooks for AI related incidents
- Assist with AI vendor and cloud services assessments
Qualifications:
\#emcor
- Seven years minimum experience in cybersecurity, security engineering and security architecture
- Three years minimum experience securing cloud platforms, APIs, and CI/CD
- Working knowledge of AI/ML concepts
- Experience with security monitoring and detection, and incident response
- CISSP, CCSP, or GIAC or equivalent certification preferred
- Ability to effectively communicate and interact with personnel at all levels
- Must be capable of delivering a very high level of customer service
Equal Opportunity Employer: As a leading provider of mechanical and electrical construction, facilities services, and energy infrastructure, we offer employees a competitive salary and benefits package and we are always looking for individuals with the talent and skills required to contribute to our continued growth and success. Equal Opportunity Employer/Veterans/Disabled Affirmative Action Policy:
Please review our Affirmative Action Policy.
Notice to Prospective Employees: Notice to prospective employees: There have been fraudulent postings and emails regarding job openings. EMCOR Group and its companies list open positions here. Please check our available positions to confirm that a post or email is genuine. EMCOR Group and its companies do not reach out to individuals to help with marketing or other similar services. If an individual is contacted for services outside of EMCOR’s normal application process – it is probably fraudulent. Geographic Disclosure:
As a leading provider of mechanical and electrical construction, facilities services, and energy infrastructure, we offer employees a competitive salary and benefits package and we are always looking for individuals with the talent and skills required to contribute to our continued growth and success. Equal Opportunity Employer/Veterans/Disabled
Compensation Range: $134,000 \- $171,000\.
Other Compensation: This position is bonus eligible.
Benefits: We are committed to providing employees a comprehensive benefits package which includes medical, dental, and vision coverage, along with health savings and flexible spending accounts, life insurance, disability, a 401(k) Savings Plan, College Coach and employee assistance program.
Salary Context
This $134K-$171K range is below the median 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 EMCOR, 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 ($152K) sits 30% below the category median. Disclosed range: $134K to $171K.
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.
EMCOR AI Hiring
EMCOR has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Norwalk, CT, US. Compensation range: $171K - $171K.
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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