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About This Role
Overview:
About Suffolk
Suffolk is a national enterprise that builds, innovates, and invests. We provide value across the entire project lifecycle through our core construction management services and complementary business lines in real estate investment, design, self\-perform construction, and technology start\-up investment (Suffolk Technologies). By integrating data, artificial intelligence, and advanced technology through our Seamless Platform, we connect design, construction, and operations to deliver smarter, more predictable results and redefine how America builds.
Suffolk – America’s Contractor – is a national company with more than $9 billion in annual revenue, 3,000 employees, and 17 offices, including Boston (headquarters), New York City, Miami, West Palm Beach, Tampa, Estero, Dallas, Los Angeles, San Francisco, San Diego, Las Vegas, Herndon, U.S. Virgin Islands, and other key markets. Suffolk manages some of the most complex and transformative projects in the country, serving clients across healthcare, life sciences, education, gaming, aviation, transportation, government, mission critical, and commercial sectors. Suffolk is privately held and is led by founder, chairman and CEO John Fish. Suffolk is ranked \#8 on ENR’s list of “Top CM\-at\-Risk Contractors.” For more information, visit www.suffolk.com and follow Suffolk on Facebook, Twitter, LinkedIn, YouTube, and Instagram.
At Suffolk, we believe that our total rewards program should offer you and your family the support you need when it matters most. That’s why we have created a program that provides employees with access to a wide variety of options that can be personalized to support you and your loved ones physically, emotionally, and financially.
Benefits include, competitive salaries, auto allowances and gas cards for certain roles, access to market leading medical and emotional and mental health benefits, dental, and vision insurance plans, virtual care options for physical therapy and primary care, generous paid time off, 401k plan with employer match and access to expert financial resources, company paid and voluntary life insurance, tax deferred savings accounts, 10 backup daycare days each year, short\- and long\-term disability, commuter benefits and more. For more information, click here. Role
Join Suffolk’s project teams across the U.S. as the on\-site catalyst who turns AI ideas into working reality. Partnering with each project’s AI Champion (Project Manager or Superintendent), you’ll uncover pain points, redesign workflows, and deploy AI agents that cut down reporting, accelerate RFIs, simplify lookahead planning, progress updates, materials tracking, and more. When needed, you will develop user stories and coordinate development with the central AI Studio. You’ll help advance the vision of the “Construction Site of the Future,” showing how agentic AI will transform project operations.
Location: Las Vegas, Nevada
Responsibilities:
- Opportunity hunting and workflow redesign – Lead discovery; map value streams, assess process and data maturity, and log low\-effort/high\-impact AI use cases.
- Process and data maturity assessment – Evaluate jobsite’s current workflows and underlying data; surface gaps that block AI adoption and develop phased improvement plans with Operations Excellence to establish the right process baseline before deploying agents.
- Assess the market solutions – Evaluate off\-the\-shelf and platform tools; launch pilots, measure impact, and scale wins.
- Rapid AI\-agent builds – Convert user stories into MVP agents in AWS Bedrock/Sagemaker, ChatGPT Enterprise, and other frameworks within days, with heavy use of AI coding assistants; wire them to Teams/SharePoint on the front end and Databricks Lakehouse , AWS services, or other sources on the back end.
- Enterprise\-grade engineering \& LLMOps – Build Agentic RAG pipelines backed by Opensearch and other data stores; automate infra with GitHub Actions; monitor latency, cost, adoption, and drift.
- Data integrations – Partner with Data Engineering to design and maintain ETL pipelines, API integrations, and event\-driven connectors feeding RAG and agents.
- Cross\-cloud orchestration – Blend AWS Bedrock, OpenA and Azure OpenAI behind secure custom connectors; package agents for seamless rollout.
- Change enablement – Train crews, gather feedback, iterate, and track adoption and ROI metrics; apply influence model principles to embed agents into daily routines and SOPs, and track behavior change KPIs.
- Stakeholder communication – Brief project leadership and clients on agent impact in clear business terms; contribute use cases and playbooks for Suffolk’s “Construction Site of the Future.”
- Escalation \& hand\-off – Draft clear user stories, data specs, and acceptance criteria for any complex solution that requires the central AI Solution Engineers or Data Engineering / Data Science team to lean in. Work closely with those teams to turn prototypes into enterprise\-ready scaled applications.
- Compliance – Ensure every on\-site AI tool meets Suffolk’s information security, data governance, and client confidentiality standards.
\#SUFFOLKHIRING
Qualifications:
- 4–6\+ years in AI engineering / full\-stack data applications or data science, including 2\+ years building production LLM/RAG/Agentic solutions.
- Bachelor’s in CS, Engineering, Physics, or a related field; Master’s preferred.
- Prior hands\-on work in construction or heavy process industries (manufacturing, oil \& gas, chemicals) is a significant plus.
- Demonstrated process excellence background (Lean/Six Sigma Green Belt a huge plus) with experience diagnosing process and data gaps and supporting change management plans with Operations Excellence.
- Strong facilitation and communication skills.
- Hands\-on expertise with Claude Code, Devin AI, MS Copilot, and other coding assistants
- Programming \& data stack: Python, SQL, Fast API, Databricks Lakehouse, vector stores.
- DevOps \& IaC: GitHub Actions Terraform or CloudFormation automation or comparable CI/CD tooling; strong Git/GitHub workflow discipline.
- Integration \& ETL skills: Foundational understanding of ETL/ELT design, Airflow or Databricks Workflows, and REST/GraphQL API development; proven collaboration with Data Engineering on source\-to\-lake and lake\-to\-agent pipelines.
- Willing and able to travel and work on active jobsites.
Working Conditions:
While performing the duties of this job, the employee is regularly required to sit for long periods of time; talk or hear; perform fine motor, hand and finger skills in the use of a keyboard, telephone, or writing. The employee is frequently required to stands; walk; and reach with arms and/or hands. Specific vision abilities include close vision, distance vision, depth perception and the ability to adjust focus. The employee will spend their time in an office environment with a quiet to moderate noise level. Job site walking.
EEO Statement:
Suffolk provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, pregnancy or maternity, national origin, citizenship, genetic information, disability, protected veteran, gender identity, age or any other status protected by law. This policy applies to recruiting, hiring, transfers, promotions, terminations, compensation, benefits, and all other terms and conditions of employment. Suffolk will not tolerate any unlawful discrimination toward, or harassment of, applicants or employees by anyone at Suffolk, or anyone working on behalf of Suffolk.
Compensation Information:
The expected salary range for this position (Site AI Engineer) in US\-NV\-Las Vegas is between $150,000 and $182,000 USD. This represents the typical salary range for this position and is just one component of Suffolk’s total compensation package. Actual salaries may be based on several factors including, but not limited to, skill set, experience, education and other qualifications. Suffolk offers a comprehensive benefits package as part of its overall compensation strategy. Salary ranges may differ by geography and are reviewed regularly to reflect market trends.
Salary Context
This $150K-$182K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Suffolk Construction, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($166K) sits 7% below the category median. Disclosed range: $150K to $182K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Suffolk Construction AI Hiring
Suffolk Construction has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Temple, TX, US, Boston, MA, US, Las Vegas, NV, US. Compensation range: $179K - $282K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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