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About This Role
Who We Are:
Core Spaces (Core) is more than a real estate company; it’s a people company. Where building relationships is just as important as building properties. From researchers and architects to designers and operators, Core is made up of risk takers and dreamers who are on a mission to invent the future of living. Our six cultural values guide us every day and we strive to live them in everything we do: PUSH (Invent Better Places to Live), GRIT (We Got This), LIFT (Help Each Other Win), SHINE (Bring Your Full Self), UPHOLD (Never Break Trust), GROW (Always Get Better). Everything Core does stem from this culture of collaboration and innovation, and the drive to constantly improve the resident experience. This unique approach has led to creating spaces and services that are redefining the way people live.
What We Do:
Founded in 2010, Core is a vertically integrated real estate investment manager focused on acquiring, developing, and managing across the student housing and build-to-rent (“BTR”) sectors. Its residential communities feature world-class amenities, progressive design, and hospitality-driven service. Core’s student housing portfolio includes over 59,000 beds currently owned or managed, with a pipeline of over 50,930 beds in various stages of development. Core’s BTR division has over 3,000 homes under development, now leasing or in its pipeline in high growth metros nationwide. For more information, visit www.corespaces.com.
- *Portfolio and pipeline numbers as of Q4 2025*
Benefits That Matter:
- A culture that provides you with a sense of belonging
- Hybrid or remote work options may vary by role to support work-life balance
- Competitive pay that values your contributions
- Incentives designed to reward your achievements
- Paid flexible PTO to disconnect or celebrate life milestones
- Paid 14+ holidays, including your birthday, to disconnect and celebrate
- Paid Parental Leave that begins after 90 days
- Paid volunteer time off to give back to your community
- Monthly workshop weeks; fewer meetings & more collaboration
- Robust health plan options that begin within at least 30 days of your employment
- Monthly phone reimbursement
- Wellness allowance and perks, including a yearly subscription to a meditation app
- An environment that provides you a voice to share your perspectives
- Employee Assistance Program (EAP) for access to confidential support services
- Company retirement options including 401(k) + matching & Roth account option
Position Overview:
The AI Architect & Engineering Director is a hands-on technical leader responsible for designing, building, and deploying artificial intelligence solutions across the organization. This role blends deep practical experience with Databricks-based machine learning, modern AI platforms, and Microsoft’s AI ecosystem, with the ability to translate business needs into working, production-grade systems.
This is not solely an advisory role. The ideal candidate actively architects, prototypes, and implements AI solutions, while also shaping long-term strategy, governance, and adoption. They will serve as both the principal AI engineer and the strategic leader defining how AI is responsibly applied across operations, development, leasing, and asset management.
What You Will Do:
Hands-On AI Architecture & Development
- Design, build, and maintain end-to-end AI solutions using Databricks, including:
+ Feature engineering pipelines
+ ML model training, evaluation, and tuning
+ Model deployment and monitoring in production
- Develop machine learning models directly in Databricks (e.g., predictive models, classification, forecasting, NLP, and applied generative AI use cases)
- Leverage Databricks AI capabilities (MLflow model registry, feature stores, notebooks, jobs, and orchestration)
- Build and integrate AI solutions with existing enterprise systems (Yardi, Salesforce, ERP, BI platforms, internal applications)
- Prototype quickly, validate value, and harden solutions for production use
AI Platforms & Tooling
- Design and implement AI solutions using:
+ Databricks (core requirement)
+ Microsoft Copilot Studio and related Microsoft AI services
+ Azure-native data and AI services where appropriate
- Evaluate when to use traditional ML vs. generative AI vs. automation
- Own the technical decisions around model selection, architecture, and scalability
AI Strategy
- Define and maintain the enterprise AI roadmap based on what can be realistically built and supported
- Identify high-value, practical AI use cases tied to measurable business outcomes (cost reduction, revenue lift, operational efficiency)
- Balance experimentation with production readiness
Governance, Reliability & Responsible AI
- Implement practical AI governance that does not slow delivery, including:
+ Model versioning and lineage
+ Performance monitoring and drift detection
+ Data privacy and security controls
- Ensure AI systems are explainable, auditable, and aligned with internal standards
Execution & Delivery
- Oversee the development and deployment of AI products, tools, and automations
- Manage vendor relationships with AI platforms, cloud providers, and consulting partners
- Build and lead a high-performing AI team (engineers, data scientists, analysts)
- Create documentation, playbooks, and best practices for AI adoption across the company
- Occasional travel may be necessary as needed
- Perform all other duties and tasks as assigned by management
- Must be able to complete all physical requirements of this role with or without a reasonable accommodation
Ideally, You'll Have:
- 5+ years in a senior leadership role designing and deploying AI systems at scale
- Proven, hands-on experience building ML models in Databricks (not just managing teams that do)
- Deep experience with PySpark/Spark ML, modern Python ML frameworks, and end‑to‑end ML lifecycle management, from model training through deployment and ongoing monitoring
- Experience integrating ML/AI solutions into production systems
- Experience translating business problems into technical solutions and back again
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field required, or equivalent practical experience
You'll crush it if you have experience with
- Microsoft Copilot Studio or Microsoft AI tooling
- Applying AI in real estate, finance, operations, or asset-heavy industries
- Machine Learning Ops patterns and CI/CD for data and ML workloads
Organizational Structure
Reports to: Vice President, Data Strategy
Direct Reports: N/A
Disclaimer:
Please note that job responsibilities, reporting lines, and duties outlined in this job description are subject to change to meet the evolving needs of the organization.
As an Equal Opportunity Employer, Core Spaces celebrates diversity and is committed to creating an equitable and inclusive environment, which creates a sense of belonging for all employees. We do not discriminate and believe every individual should be proud of who they are and the community they represent.
Pay Range: USD $205,000.00 - USD $215,000.00 /Yr. Additional Compensation: Employees may be eligible for discretionary bonuses, typically up to 20% of base salary annually, depending on individual and organizational performance. Compensation Disclosure:
The compensation range listed reflects the base salary or hourly rate that we reasonably and in good faith expect to offer for this role at the time of posting. Actual compensation may vary based on factors such as education, experience, skills, certifications, seniority, geographic location, and business needs.
This role may be eligible for additional forms of compensation, including bonuses, commissions, stipends, or non-cash incentives, depending on position and performance. Benefits may include health insurance, retirement plans, paid time off, and other role-based offerings, subject to eligibility requirements.
All compensation components are subject to change based on business needs or market conditions.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 37,339 AI roles we're tracking, AI Architect positions make up 1% of the market. At Core Spaces, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $200,526 based on 46 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($210K) sits 5% above the category median. Disclosed range: $205K to $215K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Core Spaces AI Hiring
Core Spaces has 1 open AI role right now. They're hiring across AI Architect. Based in Chicago, IL, US. Compensation range: $215K - $215K.
Location Context
AI roles in Chicago pay a median of $203,250 across 214 tracked positions. That's 7% above the national median.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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