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About This Role
About Barton Malow
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Barton Malow is a builder. For over 100 years we have delivered some of the most complex construction projects in North America — schools, hospitals, stadiums, manufacturing plants, and industrial facilities. Today we are doing something most construction companies are not: rebuilding how we build software, with AI at the center. Our APEX team owns the AI and engineering platform that the rest of the company builds on, and we are investing seriously in it.
About the role
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We are hiring a Senior AI Engineer to build the systems that make AI agents do real, reliable work across Barton Malow. This is a deep, hands\-on, senior individual\-contributor role. You will own the implementation of our agentic platform — the harness, the agent pipelines, the evaluation infrastructure, and the production systems that turn AI from a prototype into something teams depend on every day.
By "harness" we mean the machinery that makes autonomous AI reliable: the orchestration loop, the tool and data integrations, the testing and evaluation gates, the observability layer, and the deployment infrastructure around the models. Building that machinery — and keeping it running in production against real data and real workflows — is the job.
This is not a role at a software company, and it is not a role where you write a little code on the side. It is a role building the foundation an entire organization will run AI on for the next decade. The portfolio is messy, the problems are real, and the mandate is clear.
What you'll do
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Build the harness. Implement the core machinery that makes AI agents reliable: the orchestration loop, the tool\-execution and integration layer, evaluation gates, state persistence, error recovery, and the observability that makes agent behavior legible. The models change; the harness you build is what lasts.
Ship agentic systems to production — and keep them reliable. Take AI systems from prototype to production: deployment, monitoring, rollback, controlled release, and the self\-healing loops that detect and recover from failures. The bar is not a demo — it is autonomous systems running against real Barton Malow data and workflows, reliably, with you accountable for correctness and uptime.
Build the evaluation and verification infrastructure. Verification is what separates a demo from production. You will build the evaluation harness, the regression suites, the deterministic gates (tests, linters, contract checks), and the automated judging infrastructure that lets us prove AI output is correct before it ships.
Build the platform substrate. Implement the production components that connect AI agents to Barton Malow's real systems and data — Autodesk, SAP, Databricks, and a growing set of external services — and the interfaces people use to work with those agents.
Set the engineering bar by example. As one of the most senior engineers on the team, you set the implementation patterns, write the reference code others build on, review at depth, and raise the quality of everyone working alongside you.
What we're looking for
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- 10\+ years in software engineering, with demonstrated depth as a senior individual contributor who ships complex systems to production
- Hands\-on experience building and operating production AI or agentic systems — not just using AI tools in your own workflow. You have taken an LLM\- or agent\-powered system to production and kept it running
- Experience implementing the components behind reliable AI systems: orchestration, tool/function calling, evaluation, context management, and handoffs between steps or agents
- Strong software engineering fundamentals — testing, CI/CD, observability, error handling — applied to the realities of nondeterministic AI systems
- Production cloud operations, ideally AWS: architecting, deploying, and operating real workloads
- Data and integration experience: building pipelines that move data reliably between enterprise systems (ERP, SaaS APIs, data warehouses or lakehouses)
- A pragmatic builder's instinct: you build the reusable thing, iterate in production, and know when shipping behind a flag beats perfecting on paper
- Strong communicator who can work with engineers, managers, and executives and adjust the level for each
Nice to have
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- Hands\-on experience with an agent framework or SDK and with multi\-agent or planner/generator/evaluator architectures
- Experience building evaluation frameworks or automated AI\-judging systems
- Experience with Databricks or a similar lakehouse platform
- Experience building tool\-integration layers (e.g., MCP servers) between AI systems and enterprise applications
- Experience in a non\-software\-company engineering organization — internal tools, corporate IT transformation, or similar
- Open\-source contributions or public work that shows how you build
Why this role
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You will build the platform an entire organization runs AI on, with a clear mandate and a direct line to the Director of APEX. The platform is early — that is the appeal. You are not maintaining someone else's infrastructure; you are building the harness Barton Malow will use for the next decade. When the models get better, the infrastructure you build is what lets the company capture it.
Barton Malow is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability status, genetic information, protected veteran status, or any other legally protected characteristic.
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 Barton Malow Company, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
Barton Malow Company AI Hiring
Barton Malow Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Southfield, MI, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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