Interested in this AI/ML Engineer role at Core States Group?
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About This Role
At Core States Group, the work you do matters to the clients we serve, the communities we support, and the teams who rely on one another to deliver with excellence. Our Cause is powerful: to create a world where all environments improve everyday life.
Why Professionals Choose Core States GroupHere's what you can expect:* COMPENSATION – Competitive wages and bonus programs
- RETIREMENT – We offer a great 401(k) match to help you invest in your future and grow your retirement savings with confidence.
- HEALTHCARE – Excellent health benefits, life insurance options, and supplemental benefit options, giving you the flexibility to choose the coverage that best fits your needs and lifestyle.
- VACATION – Generous PTO time to help you recharge, relax, and maintain a healthy work\-life balance.
- PERKS – Team wellness challenges, Costco membership reimbursement, half\-day Fridays during summer months \+ much more!
- WORK\-LIFE BALANCE – A flexible work environment that supports a healthy work\-life balance, whether you’re in the office, working remotely, or somewhere in between.
- PAID PARENTAL LEAVE – Four weeks of fully paid parental leave to give you time to bond with your new addition and support your growing family.
- TUITION REIMBURSEMENT – Advance your education with support! We offer tuition reimbursement to support your learning journey.
Summary of the Role
The AI Platform Engineer, will be a member of the Digital Transformation team at Core States and report to the Director of that group. The AI Platform Engineer, will be responsible for building and maintaining the connectivity layer that makes the firm’s core systems accessible to AI, including integration services and MCP servers for business intelligence, ERP, design, and document management platforms, along with the authentication, logging, and evaluation infrastructure those integrations depend on. High\-level architecture is owned by the Director of Digital Transformation; this role advises on design, implements it, and tests it rigorously. They will be expected to build integration services against enterprise APIs, implement per\-user authorization so AI tools respect the permissions already defined in each source system, establish the auditing and benchmarking practices that keep AI\-generated answers trustworthy, and support AI\-enabled features in the team’s internal applications. Responsibilities* Design, build, and maintain integration services and MCP servers that expose firm systems — including Power BI and Fabric, Deltek Vantagepoint, Autodesk Platform Services, Egnyte, and internal applications — to approved AI platforms
- Implement per\-user authorization so AI access respects the permissions already defined in each source system, using Microsoft Entra ID and OAuth 2\.0 delegated and on\-behalf\-of flows
- Build audit logging, monitoring, and access reporting for AI\-to\-system traffic
- Establish evaluation and benchmarking infrastructure, including automated tests for connector correctness, regression testing of model\-facing behavior, and ongoing measurement of answer accuracy
- Evaluate vendor\-supplied connectors and MCP servers against in\-house development, and make clear build\-versus\-adopt recommendations
- Implement and maintain the entity resolution and crosswalk services that link records across systems, working from the semantic model defined with the Director of Digital Transformation and the data engineer
- Advise on architectural decisions by surfacing tradeoffs, risks, and implementation constraints for the Director of Digital Transformation to decide
- Develop and support AI\-enabled features in internal applications, including LLM API integration, structured outputs, and prompt and version management
- Write and maintain automated tests, technical documentation, and deployment automation for all platform components
- Partner with the Technical Program Manager on scope, estimates, and delivery commitments, and with system owners on data access and governance questions
- Other duties as assigned
Qualifications
We’re looking for talented professionals who are eager to grow their skills and contribute to high\-quality, meaningful work. If you’re driven, collaborative, and ready to take the next step in your career, we want to hear from you. The following qualifications will help you succeed in this role:* Bachelors degree in Business, Information Technology, Engineering, or relevant field; or equivalent professional experience
- 5\+ years of professional software development experience, with significant time spent on backend services and system integration
- Demonstrated experience integrating enterprise systems through REST APIs, including authentication, pagination, rate limiting, and error handling
- Experience implementing OAuth 2\.0 and OpenID Connect flows in a Microsoft Entra ID environment, including delegated and on\-behalf\-of scenarios
- Experience building, deploying, and operating services in Microsoft Azure
- Hands\-on experience integrating LLM APIs into production software (OpenAI, Anthropic, or comparable)
- Experience with MCP server development, or comparable tool and function\-calling integration work, preferred
- Experience building automated test suites or evaluation harnesses for systems with non\-deterministic output preferred
- Experience in the AEC industry or another project\-based professional services business preferred
- Strong C\# and .NET Core development skills; Python or TypeScript for integration and tooling work
- Working knowledge of REST API design and consumption, OAuth 2\.0 and OpenID Connect, and secure token handling
- Microsoft Entra ID application registrations, app roles, scopes, consent, and the implications of Conditional Access policies
- SQL Server and relational data modeling, including comfort assessing query performance
- Azure services relevant to hosting integration workloads, such as App Service, Functions, Key Vault, API Management, and Application Insights
- Familiarity with the Model Context Protocol and tool and function\-calling patterns, retrieval\-augmented generation concepts, and prompt and version management
- Source control, CI/CD, and infrastructure\-as\-code practices (Azure DevOps, GitHub Actions, Bicep or Terraform)
- Familiarity with the APIs of relevant enterprise platforms preferred, including Power BI and Fabric, Deltek Vantagepoint, Autodesk Platform Services, Egnyte, and Microsoft Graph
Work Location
This position is currently hybrid\-remote in the Philadelphia region, with occasional requirements to meet in person with coworkers for important meetings or team\-building activities at our King of Prussia or Philadelphia offices. Company Overview
Core States Group designs, builds, and manages programs and projects across North America and has been recognized on top lists throughout the architecture, engineering, and construction (A/E/C) industry. Established in 1999, the company offers integrated solutions with seamless continuity, and many of the world’s most notable brands count on Core States Group to deliver speed to market, cost certainty, and compelling design.
With 24 offices across the United States and Canada, Core States Group is an industry powerhouse that drives innovation and improves everyday life for countless people across several markets, including Community, Convenience, Distributed Generation, Financial, Grocery, Housing, Industrial, Restaurants, Retail, Semiconductor, Tribal, Zero\-Emission Vehicle (ZEV) Infrastructure, and more. Visit us at www.core\-states.com. A Note to Candidates
Please do not be concerned if you do not meet every requirement. At Core States we are dedicated to building a diverse and inclusive workplace, so if you are excited about this role but your experience does not align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
This position will remain open until it is filled.
Salary Context
This $140K-$165K range is below the median 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 Core States Group, 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 ($152K) sits 29% below the category median. Disclosed range: $140K to $165K.
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.
Core States Group AI Hiring
Core States Group has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Philadelphia, PA, US, Somerville, NJ, US, King of Prussia, PA, US. Compensation range: $125K - $165K.
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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