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About This Role
Compensation Range:
$120,000\.00 \- $170,000\.00 Annual SalaryJob Description Summary:
We are seeking an Applied AI Engineer to build and deliver practical AI solutions that drive automation, productivity, and business value. This role is focused on turning business needs into working solutions \- from intelligent workflows and copilots to agent\-based tools and AI\-enabled applications. The ideal candidate is both technically strong and highly hands\-on, with the ability to design, prototype, build, and deploy scalable solutions that solve real business problems.Job Description:
Key Responsibilities
- Build and deploy AI\-powered automations and agent\-based solutions that improve business processes and productivity.
- Design and implement practical AI workflows using LLMs, retrieval, tool/function calling, orchestration, multi\-agent patterns, integrations, and related technologies.
- Integrate AI capabilities into enterprise systems, applications, and business processes to support real\-world adoption and measurable value.
- Test, monitor, troubleshoot, and continuously improve deployed AI solutions for reliability, usability, and performance.
- Collaborate with business and technical stakeholders to refine requirements and translate them into working solutions.
- Contribute reusable patterns, engineering practices, and responsible AI approaches that support scalable solution delivery.
Skills, Experience, and Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
- 3\+ years of experience in software engineering, AI engineering, machine learning engineering, or similar hands\-on technical roles.
- Hands\-on experience building and deploying AI\-enabled applications, automations, or intelligent workflows in an enterprise environment, including delivery within defined solution direction and architectural guardrails.
- Experience with AI frameworks, APIs, and tools for LLMs, retrieval, tool/function calling, orchestration, or multi\-agent workflows.
- Familiarity with frameworks and protocols such as LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, MCP, or similar tools is preferred.
- Experience with enterprise cloud and AI development platforms such as Microsoft Foundry, Databricks, or similar enterprise ecosystems is preferred.
- Strong programming and engineering skills, including CI/CD, deployment automation, monitoring, and operational support practices for AI or software solutions.
- Strong problem\-solving, communication, and stakeholder collaboration skills.
- Construction industry experience is preferred but not required.
Summary of Benefits:
This role is eligible for the following benefits: medical, dental, vision, 401(k) with company matching, Employee Stock Ownership Program (ESOP), individual stock ownership, paid vacation, paid sick leave, paid holidays, bereavement leave, employee assistance program, pre\-tax flexible spending accounts, basic term life insurance and AD\&D, business travel accident insurance, short and long term disability, financial wellness coaching, educational assistance, Care.com membership, ClassPass fitness membership, and DashPass delivery membership. Voluntary benefits include additional term life insurance, long term care insurance, critical illness and accidental injury insurance, pet insurance, legal plan, identity theft protection, and other voluntary benefit options.
Anticipated Job Application Deadline:
07/17/2026
### About Us
Welcome to Swinerton—Building Excellence Since 1888
Swinerton is a 100% employee\-owned construction company with a legacy of shaping skylines, transforming communities, and delivering award\-winning projects across the United States. Since our founding in 1888, we have remained forward\-thinking, operating with accountability, integrity, and a passion for building what matters.
As an integrated ecosystem of expertise, Swinerton brings together talented professionals from diverse backgrounds to deliver innovative and efficient construction solutions. Our collaborative, interdisciplinary approach sets us apart, allowing us to respond to any project challenge—regardless of size, location, or complexity.
When you join Swinerton, you become part of a dynamic network of employee\-owners who are committed to excellence, safety, and continuous growth. We invest in your development, offer competitive benefits, and empower you to make a meaningful impact on every project and in every community we serve.
Discover how you can build your future with Swinerton. Explore our open positions and join a team that leads with ownership, integrity, and a shared commitment to success.
Swinerton—Building is not just what we do; it is who we are.
www.swinerton.com
Swinerton is an Equal Employment Opportunity, Minority, Women, Disability, and Veteran Employer.
Our Company is an equal opportunity and affirmative action employer. We have a commitment to provide equal hiring, training, compensation, promotion, transfer, layoff and recall benefits to all individuals without regard to gender, race, color, religion, age, mental or physical disability, medical condition, genetic information, sex, sexual orientation, gender identity and expression, national origin, marital or domestic partner status, veteran status, or any other characteristic protected under federal or state laws or local ordinance or regulation.
We are committed to the safety and security of everyone interested in our company–including those who visit us online. Please note that Swinerton will only invite you to submit work history and personal information related to employment through our career portal Workday (which requires the set\-up of a login) and communication will only come from individuals with an email address ending in @swinerton.com. If you receive fraudulent communication please inform us by emailing [email protected].
Salary Context
This $120K-$170K 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 Swinerton, 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 ($145K) sits 34% below the category median. Disclosed range: $120K to $170K.
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.
Swinerton AI Hiring
Swinerton has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Concord, CA, US. Compensation range: $170K - $170K.
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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