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About This Role
AI Solutions \& Automation Engineer
Do you have a passion for solving real\-world business problems with AI and automation?
T.M. Bier \& Associates is seeking a practical and innovative AI Solutions \& Automation Engineer to help expand the effective use of artificial intelligence and automation across our organization.
In this role you will work with each department to understand existing workflows, identify repetitive or inefficient processes, and develop targeted tools and automations that improve productivity, consistency, and cost efficiency.
The ideal candidate will have a strong technical foundation including programming experience, and an interest in applying existing AI technologies to business and engineering challenges.
The Company
T.M. Bier \& Associates, Inc. (TMBA) is one of the largest independent and privately owned Building Management System and Control Systems Engineering and Support firms in the New York Metropolitan area.
Since our founding in 1977, TMBA has specialized in providing turnkey Building Automation Systems and Energy Cost Reduction Measures. We provide innovative design, installation, and true 24/7 service to commercial office, high\-rise residential, hospital, hotel, university, and other institutional clients. Our loyal customer base is a testament to TMBA’s quality products, technical expertise, and dedicated service and is the foundation of our continued growth.
Responsibilities
Incorporate the practical application of existing AI platforms, models, and APIs into company\-wide workflows.
- Meet with employees and department leaders regularly to understand workflows, challenges, and repetitive tasks.
- Identify opportunities to improve efficiency and reduce costs through AI, automation, and software tools.
- Design, develop, test, and implement targeted AI\-powered applications, scripts, integrations, and automated workflows.
- Use programming languages, APIs, low\-code platforms, and existing AI services to connect systems and automate processes.
- Help improve the company’s use of ChatGPT, Microsoft Copilot, SharePoint, Teams, Power Automate, and other approved platforms.
- Research and evaluate available AI and automation products based on cost, compatibility, security, and business value.
- Develop training materials and assist employees with adopting new AI tools and automated processes.
- Track time savings, cost reductions, productivity improvements, and return on investment.
- Collaborate with Software Engineering, IT, cybersecurity, and company leadership to support responsible and secure AI use.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Information Systems, or similar.
- Experience with a programming or scripting language such as Python, JavaScript, C\#, or similar.
- Familiarity with generative AI tools and large language models, including ChatGPT, Microsoft Copilot, Claude, Gemini, or similar platforms.
- Understanding of APIs, software integrations, data processing, and workflow automation.
- Ability to analyze an existing business process and translate operational needs into a practical technical solution.
- Strong analytical, troubleshooting, and problem\-solving skills.
- Strong verbal and written communication skills.
- Excellent organizational skills and attention to detail.
- Ability to explain technical concepts to both technical and nontechnical employees.
- Ability to manage multiple projects and work independently.
- Experience with Microsoft Copilot, SharePoint, Teams, Power Automate, Power Apps, Copilot Studio, REST APIs, JSON, databases, or Git is preferred.
- Experience in building automation, HVAC, controls, engineering, construction, or field\-service operations is preferred.
Work Location
· Glen Cove Office
· Commute to Rockefeller Center, NYC office as needed.
Benefits
- Medical, Dental and Vision Insurance
- Life Insurance, Short\-term and Long\-term Disability Insurance
- Paid Time Off, Paid Holidays
- Employer Matched 401K Plan
- Profit Sharing Plan
Job Type
Full\-time, Exempt
Pay
$70,000\.00 to $90,000\.00
Job Type: Full\-time
Pay: $70,000\.00 \- $90,000\.00 per year
Benefits:
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Application Question(s):
- Do you have experience with programming and AI tools?
Education:
- Bachelor's (Preferred)
Ability to Commute:
- Glen Cove, NY 11542 (Required)
Work Location: In person
Salary Context
This $70K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At T.M. Bier and Associates, 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. This role's midpoint ($80K) sits 63% below the category median. Disclosed range: $70K to $90K.
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.
T.M. Bier and Associates AI Hiring
T.M. Bier and Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Glen Cove, NY, US. Compensation range: $90K - $90K.
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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