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About This Role
Overview:
When you join Hines, you will embark on a career journey fueled by vision and guided by leaders who set the standards of our industry. Our legacy is rooted in innovation and excellence, earning us a spot on Fast Company’s esteemed annual list of the World’s Most Innovative Companies, as well as recognition as one of U.S. News \& World Report’s Best Companies to Work For in 2024\. Discover endless opportunities to grow and make your mark at Hines.
Responsibilities:
We are looking for an AI Embedded Business Automation Engineer to work within functional departments across the organization to uncover opportunities where AI, automation, and better system design can meaningfully improve business operations. This role will work directly with the business teams to understand how work happens today, pinpoint manual handoffs, rework, delays, and exception\-heavy processes, and then design, prototype, and deliver technical solutions that make day\-to\-day work faster, smarter, and more scalable.
As part of IT, this role is expected to build within enterprise standards for infrastructure, data, integration, automation, information security, governance, and production support. The work will range from AI\-enabled workflow redesign and secure data pipelines to intelligent agents, system integrations, and automations that are reliable enough to run in production and useful enough to become part of how teams operate. Responsibilities include, but are not limited to:
- Partner directly with business stakeholders and subject matter experts to understand current workflows, identify manual handoffs, rework, delays, exception\-heavy processes, and other improvement opportunities, and prioritize AI automation efforts based on value, feasibility, risk, and scalability.
- Co\-design AI\-enabled process improvements with SMEs, translating operational challenges into practical workflows that improve efficiency, accuracy, user experience, and business capacity.
- Translate business requirements into clear technical specifications, solution designs, implementation plans, and delivery milestones, then build agentic and automation solutions end\-to\-end in partnership with business teams, teammates, and senior engineers.
- Rapidly prototype, test, and iterate on AI\-driven automations, assistants, agents, and workflow solutions using Python, LLM APIs, RAG patterns, agent frameworks, workflow orchestration, and enterprise platforms.
- Develop and maintain integrations with enterprise systems such as SharePoint, Teams, Outlook, ERP platforms, Salesforce, internal applications, APIs, and data sources to enable end\-to\-end intelligent workflows.
- Design and implement trustworthy AI solution components, including prompt orchestration, RAG pipelines, document ingestion, vector search, backend services, automation logic, data pipelines, models, and secure access patterns.
- Support adoption through SME enablement, practical training, feedback loops, and usage\-based refinement so business teams actively shape and sustain the workflows being introduced.
- Embed security, privacy, compliance, and responsible AI controls throughout the solution lifecycle, including data classification, least\-privilege access, secrets management, auditability, and safe data handling.
- Remain accountable for production quality by deploying, monitoring, troubleshooting, optimizing, and continuously improving AI and automation solutions with a focus on reliability, performance, security, governance, cost management, and real\-world usage.
- Work cross\-functionally with engineering, product, security, legal, and business teams to move solutions from concept through pilot, production launch, adoption, support, and ongoing enhancement.
- Document solution architecture, implementation decisions, support procedures, runbooks, and best practices, and create repeatable agent, automation, integration, and data patterns that scale across functions while aligning with IT governance and architecture standards.
- Define success metrics and measure solution impact, including cycle\-time reduction, quality improvements, adoption, reliability, and unlocked business capacity.
Qualifications:
Minimum Requirements include:
- Bachelor’s degree in Computer Science, Engineering, Information Technology, Data Science, Mathematics, or a related field; equivalent practical experience may also be considered.
- 3\+ years of experience in software engineering, automation engineering, cloud engineering, data engineering, integration development, or related technical roles, including experience delivering AI or Generative AI solutions in a business environment.
- Experience building workflow automations, APIs, integrations, copilots, assistants, or AI\-enabled business applications using Microsoft, Azure, OpenAI, ChatGPT Enterprise, or comparable enterprise platforms.
- Experience supporting production systems, partnering with cross\-functional stakeholders, and translating business needs into secure, scalable technical solutions.
- Strong product judgment, communication skills, and delivery orientation, with the ability to release useful increments quickly, recognize when a more durable solution is needed, and write specs, docs, and updates that are clear without additional explanation.
- Demonstrated ability to deliver reliable code, services, integrations, or automations used in day\-to\-day business operations.
- Required coding proficiency in Python, with the ability to write clean, tested, maintainable code and contribute effectively to shared production codebases.
- Hands\-on experience applying LLMs and Generative AI to real business problems, including prompt engineering, LLM APIs, retrieval\-augmented generation, AI\-enabled workflows, prototypes, assistants, or agents.
- Experience using AI\-assisted development tools and building tool\-using workflows or custom agents that leverage MCP or similar agent integration patterns and connect securely to enterprise systems, APIs, data sources, and business applications.
- Demonstrated ability to turn business problems into technical specifications, solution designs, implementation plans, and working software that can be explained clearly to non\-technical partners.
- Working knowledge across several core enterprise engineering areas, including cloud infrastructure, data engineering, SQL, pipelines, integration patterns, automation tooling, IAM, APIs, and enterprise application development.
- Familiarity with information security fundamentals, including authentication, authorization, encryption, secrets management, safe data practices, least\-privilege access, auditability, and responsible AI guardrails.
- Compensation: New York City \- $130,000 \- $175,900; Houston \- To be determined based on experience
Closing:
Hines is a global real estate investment, development and property manager. The firm was founded by Gerald D. Hines in 1957 and now operates in 28 countries. We manage a $92\.3B¹ portfolio of high\-performing assets across residential, logistics, retail, office and mixed\-use strategies. Our local teams serve 634 properties totaling over 225 million square feet globally. We are committed to a net zero carbon target by 2040 without buying offsets. To learn more about Hines, visit www.hines.com and follow @Hines on social media. ¹Includes both the global Hines organization as well as RIA AUM as of June 30, 2022\. *We are an equal opportunity employer and support workforce diversity.* *No calls or emails from third parties at this time please.*
Salary Context
This $130K-$175K 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 Hines, 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: $130K to $175K.
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.
Hines AI Hiring
Hines has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $175K - $175K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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