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About This Role
Who We Are
Revantage, a Blackstone Real Estate portfolio company, is a global provider of corporate services.
With a corporate purpose of ‘In Pursuit of Better,’ Revantage delivers value-added services and world-class talent for Blackstone Real Estate portfolio companies, spanning diverse asset classes, including residential, logistics, office, hospitality and retail sectors. Headquartered in Chicago, the company’s footprint extends across North America, Europe and Asia Pacific.
Rooted in a commitment to collaboration and inclusivity, Revantage goes beyond traditional corporate services and acts as a trusted partner. Across offerings that include finance, technology, human resources and operations, Revantage proactively anticipates stakeholder needs, recruits exceptional talent and enables its business partners to thrive.
What We Value: Our Culture
Our people are our most important asset, enabling Blackstone portfolio companies and investments to scale and thrive. Together, we foster a workplace where everyone can be themselves, enabling them to do their best work.
At Revantage, we have exceptional people who live our values and help us pursue better every day. We offer dynamic and meaningful work, competitive compensation, benefits and flexibility. We listen and take action to ensure our organization evolves to reflect our employees’ voices and support an inclusive culture.
Our demonstrated commitment to our people and collaborative culture have earned us numerous awards as a top employer.
Our culture is built on our shared core values and commitment to be:
- Learners – We learn from our challenges and successes
- Leaders – We commit to continuous improvement
- Enthusiasts – We face challenges with optimism and believe anything is possible
- Achievers – We expect high standards for ourselves and enable the success of our teams
- Partners – We deliver value and positive impact to our partners
Grow your career with us. As a member of our team, you'll gain hands-on experience in the real estate industry and benefit from a supportive environment that fosters personal and professional growth.
WHY THIS ROLE IS VALUABLE
The AVP, Forward Deployed Engineer is responsible for partnering directly with business stakeholders, portfolio companies, and technology teams to design and deliver scalable AI, data, and cloud solutions that solve complex business challenges. This role sits at the intersection of software engineering, data engineering, and consulting, translating business requirements into production-ready solutions that drive measurable outcomes.
The ideal candidate combines strong Python engineering expertise with experience building modern cloud-native solutions on Azure. They are comfortable working directly with end users, rapidly prototyping solutions, developing scalable data pipelines, and deploying AI-enabled applications using modern data platforms such as Databricks and Snowflake.
HOW YOU ADD VALUE
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Data Science, or related field
- 7+ years of experience in software, data, or platform engineering
- Advanced Python development experience with strong software engineering fundamentals
- Experience designing and deploying cloud-native solutions in Azure
- Experience building and supporting distributed, scalable applications and microservices
- Experience developing and consuming REST APIs
- Strong understanding of event-driven architectures and serverless computing
- Experience with CI/CD pipelines, automated testing, and DevOps best practices
- Strong communication and stakeholder management skills
- Proven ability to translate business requirements into technical solutions
Preferred Qualifications
- Hands-on experience with Databricks and modern data engineering frameworks
- Experience with Snowflake data warehousing and analytics platforms
- Experience building AI/ML-enabled applications and solutions
- Familiarity with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI orchestration frameworks
- Experience with containerization technologies such as Docker and Kubernetes
- Experience with monitoring, observability, and application performance management tools
- Consulting or customer-facing solution delivery experience
- Experience working across multiple business functions and executive stakeholders
KEY RESPONSIBILITIES
- Partner with business leaders and portfolio companies to identify opportunities for AI, automation, and data-driven solutions
- Design, build, and deploy modern cloud-native applications using Python and Azure
- Develop scalable data pipelines and data products leveraging Databricks and Snowflake
- Build AI-powered solutions that improve business processes and decision-making
- Lead technical discovery sessions and translate business needs into architecture and implementation plans
- Develop APIs, microservices, and event-driven applications supporting enterprise platforms
- Drive best practices around software development, testing, security, and automation
- Collaborate closely with product managers, data engineers, architects, and business stakeholders
- Mentor engineers and contribute to technical standards and engineering excellence
- Stay current on emerging AI, cloud, and data technologies and identify opportunities for adoption
Base Compensation Range:
$127,000.00 To $170,000.00 Annually. This represents the presently-anticipated low and high end of the Company’s base compensation range for this position. Actual base compensation range may vary based on various factors, including but not limited to location and experience.
Total Direct Compensation:
This job is also eligible for discretionary bonus and incentive compensation on an annual basis.
Benefits:The Company provides a variety of benefits to employees, including health insurance coverage, retirement savings plan, paid holidays and paid time off (PTO).
The additional total direct compensation and benefits described above are subject to the terms and conditions of any governing plans, policies, practices, agreements, or other materials or documents as in effect from time to time, including but not limited to terms and conditions regarding eligibility.
Perks for You
- Competitive salary, overall compensation, and 401(k)
- Work-life balance offerings include:
+ Hybrid Work Policy + Productivity Hours – weekly meeting-free work time + Summer Fridays + Work From Anywhere Month
- In-house and external learning & development opportunities
- Generous health insurance and wellness benefits
Please review the Job Applicant Privacy Notice.
EEO Statement
Revantage is an equal opportunity employer. In accordance with applicable law, we prohibit discrimination against any applicant, employee, or other covered person based on any legally recognized basis, including, but not limited to: veteran status, uniformed servicemember status, race, color, caste, immigration status, religion, religious creed (including religious dress and grooming practices), sex, gender, gender expression, gender identity, marital status, sexual orientation, pregnancy (including childbirth, lactation or related medical conditions), age, national origin or ancestry, citizenship, physical or mental disability, genetic information (including testing and characteristics), protected leave status, domestic violence victim status, or any other consideration protected by federal, state or local law. We are committed to providing reasonable accommodations, if you need an accommodation to complete the application process, please email [email protected]
Salary Context
This $127K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Revantage, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($148K) sits 30% below the category median. Disclosed range: $127K to $170K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Revantage AI Hiring
Revantage has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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