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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 Summary
Suffolk’s AI Studio is seeking a hands\-on Engineering Manager to lead the engineering execution of our AI systems and platform services. This role combines technical leadership, people management, and hands\-on engineering. You will manage a team of AI engineers (U.S. and India) while contributing directly to architecture, code, and delivery of production AI systems.
You will focus on:* Raising engineering rigor
- Improving system architecture quality
- Ensuring scalable, reliable AI services
- Driving consistent delivery execution
This role reports to the VP, Artificial Intelligence, and partners closely with Product and Domain leaders. Scope of Responsibility
This role is accountable for:* Engineering execution quality
- Platform architecture implementation
- Team technical mentorship
- Delivery predictability
- Offshore team coordination
Responsibilities:
AI Systems Engineering Leadership* Guide the team in building production\-grade RAG pipelines and agentic systems
- Establish practical patterns for multi\-model orchestration and inference reliability
- Ensure AI systems include cost controls, fallback strategies, and evaluation frameworks
- Review and improve agent safety, tool invocation patterns, and guardrails
Architecture \& Distributed Systems Quality* Lead architecture reviews for new AI services.
- Improve API design standards, versioning practices, and event\-driven patterns
- Ensure proper handling of retries, idempotency, and asynchronous workflows
- Drive adoption of observability and monitoring best practices
- Identify and remediate architectural debt.
Team Leadership* Directly manage AI engineers in the U.S.
- Provide technical oversight and structured guidance to offshore India engineers
- Conduct code reviews and system design reviews
- Coach engineers in distributed systems thinking and production hardening
- Improve sprint execution discipline and technical accountability
Delivery \& Operational Excellence* Ensure AI solutions move from prototype to production reliably
- Partner with Product and Domain AI leads to translate requirements into well\-architected systems
- Coordinate closely with DevOps on deployment, CI/CD, and infrastructure standards
- Improve predictability in delivery timelines and system reliability
Qualifications:
Qualifications* 8 years of professional software engineering experience
- 2\+ years developing enterprise AI applications
- 2\+ years of people management or technical team leadership
- Hands\-on experience building LLM\-integrated applications
- Experience with RAG pipelines, vector databases and agentic stacks
- Strong background in backend and distributed systems design
- Strong AWS experience (Lambda, ECS/EKS, API Gateway, SQS/SNS, Step Functions, Bedrock)
- Proven ability to lead offshore or distributed teams
- Strong coding proficiency in Python and one of Java, NodeJS, Rust or Kotlin
You are* A strong senior engineer who enjoys mentoring others
- Comfortable reviewing architecture and writing production code
- Excited by applied AI, but grounded in systems engineering fundamentals
- Structured in execution and clear in communication
- Able to raise the team’s technical bar without overcomplicating systems
What Success Looks Like* AI services are production\-ready, observable, and scalable
- The offshore team operates with clarity and accountability
- Architecture decisions are consistent and well\-documented
- The AI engineering team improves in distributed systems maturity
- Delivery is predictable and high\-quality
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 (Engineering Manager, AI Platforms) in US\-MA\-Boston is between $201,000 and $282,000 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 $201K-$282K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($241K) sits 33% above the category median. Disclosed range: $201K to $282K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Suffolk Construction AI Hiring
Suffolk Construction has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Boston, MA, US. Compensation range: $222K - $282K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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