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About This Role
Corporate
Alpharetta, GASenior Data \& AI Engineer
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Founded in 1972, Meadows \& Ohly is a leading healthcare real estate services firm serving health systems and providers nationwide. With more than 250 employees across nine offices, we deliver integrated real estate, advisory, development, and management solutions tailored to the healthcare industry. Our culture is built on integrity, collaboration, hard work, and long\-term relationships, driven by exceptional people committed to doing what is best for our clients, partners, and communities.
Why Join Us?
- Awarded 2025 \& 2026 Modern Healthcare Best Places to Work
- Competitive compensation and performance incentives
- Comprehensive health, dental, and vision benefits
- 401(k) with company match
- Generous PTO and paid holidays
- Professional development and career growth opportunities
- Collaborative, people\-first culture focused on work\-life balance
Position Summary:
The Senior Data \& AI Engineer is responsible for designing, building, and scaling Meadows \& Ohly's enterprise data platform and AI capabilities. This hands\-on role expands our Snowflake data lakehouse, develops scalable data pipelines, and implements AI\-powered solutions that drive smarter business decisions across the organization. We are looking for a collaborative, curious engineer who enjoys solving complex problems, embraces continuous learning, and thrives in a culture of servant leadership. At Meadows \& Ohly, our people are committed to enhancing the lives of others through innovation, integrity, and teamwork, and we're looking for someone who shares those values while helping shape the future of our Data \& AI capabilities.
Key Responsibilities:
- Design, develop, and optimize Meadows \& Ohly's Snowflake data platform using a modern medallion architecture
- Build and maintain scalable ETL/ELT pipelines that integrate enterprise business systems and data sources
- Develop AI\-powered applications, including Retrieval\-Augmented Generation (RAG) pipelines and intelligent search capabilities
- Implement data quality, monitoring, governance, and platform observability to ensure reliable enterprise reporting
- Collaborate with business and technical teams to translate business needs into scalable data solutions
- Optimize platform performance, security, and scalability while establishing engineering best practices
- Mentor junior team members and contribute to the growth of the Data \& AI function
- Support enterprise analytics, reporting, and advanced AI initiatives across the organization
- Stay current on emerging data engineering, cloud, and AI technologies to drive continuous innovation
Qualifications:
- Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, or related field
- 5\+ years of experience designing and supporting enterprise data platforms
- Strong experience with Snowflake, SQL, and Python
- Experience developing modern ETL/ELT pipelines and cloud\-based data architectures
- Experience with AI technologies, including Large Language Models (LLMs), RAG frameworks, or vector search
- Strong analytical, communication, and problem\-solving skills
- Experience mentoring technical team members preferred
- Snowflake certification or equivalent experience preferred
- Experience in healthcare, commercial real estate, or other regulated industries preferred
Additional Requirements \& Disclaimer:
*Candidates must possess reliable transportation and maintain a valid driver’s license, as certain roles may require travel to client sites or other off\-site locations. Employment is contingent upon successfully passing applicable background, credit, and/or drug screenings, in accordance with applicable laws and position requirements.*
*Reasonable accommodations may be made to enable qualified individuals with disabilities to perform essential functions of the role. Depending on the position, employees may be required to sit, stand, walk, drive, communicate, use hands and fingers, lift or move materials in varying weight, and work in active healthcare, office, or commercial building environments. Specific vision abilities, including close vision and the ability to adjust focus, may also be required.*
*This job posting is not intended to be an exhaustive list of all duties, responsibilities, or qualifications associated with the position. Responsibilities and requirements may change based on business needs. Meadows \& Ohly is an Equal Opportunity 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 Meadows & Ohly, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Meadows & Ohly AI Hiring
Meadows & Ohly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alpharetta, GA, 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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