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About This Role
Company Description Work with Us. Change the World.
At AECOM, we're delivering a better world. Whether improving your commute, keeping the lights on, providing access to clean water, or transforming skylines, our work helps people and communities thrive. We are the world's trusted infrastructure consulting firm, partnering with clients to solve the world’s most complex challenges and build legacies for future generations.
There has never been a better time to be at AECOM. With accelerating infrastructure investment worldwide, our services are in great demand. We invite you to bring your bold ideas and big dreams and become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction managers and other professionals delivering projects that create a positive and tangible impact around the world.
We're one global team driven by our common purpose to deliver a better world. Join us.
Job Description
AECOM is seeking a Product Management Lead to own the product strategy, delivery sequencing, and outcome realization for AI\- and agentic\-enabled capabilities embedded into day\-to\-day enterprise workflows across core business operations.
This role is accountable for deciding where and how AI should be applied, including defining appropriate levels of autonomy, human\-in\-the\-loop (HITL) designs, trust thresholds, and Responsible AI guardrails. The Director ensures AI products are not only delivered, but adopted, trusted, and generating measurable business value at scale.
Operating within enterprise product standards and governance, this role partners closely with Process Intelligence \& Discovery, Responsible AI, Architecture, Data, Engineering, Delivery, and Operations teams. The Director leads Product Managers and serves as the primary steward of AI product decisions, trade\-offs, and outcomes across the Operational AI portfolio.
This is a product leadership role, not a technical architecture or program management position.
*This position offers flexibility for hybrid work schedules to include both in\-office presence and some flexibility for virtual/telecommute work to be based in Houston or Dallas, TX.*
Key Responsibilities
- Own and maintain the Operational AI and agentic product portfolio roadmap, defining where AI should assist, advise, or act autonomously, aligned to enterprise product and Responsible AI standards
- Translate process discovery insights and business demand into AI\-ready product backlogs, including autonomy levels, HITL workflows, guardrails, and success metrics
- Lead AI\-specific product trade\-off decisions, balancing value vs. risk, autonomy vs. control, model cost vs. quality, and reuse vs. time\-to\-impact
- Define product hypotheses, evaluation criteria, and trust thresholds for AI capabilities (e.g., accuracy, containment, correction rates, explainability, user confidence)
- Partner with Architecture, Data, and Engineering to shape feasible AI solutions that meet business intent, Responsible AI requirements, and operational constraints—without owning technical design
- Own adoption and behavior\-change strategy for AI products, incorporating rollout feedback, user corrections, and realized outcomes into roadmap evolution
- Establish and operate autonomy governance with Responsible AI, Legal, Risk, and Operations to determine when AI may act, advise, or must defer to human approval
- Lead and develop Product Managers, strengthening AI product craft across outcome definition, HITL design, evaluation planning, and risk stewardship
- Communicate product status, risks, and performance using AI\-relevant evidence, including adoption, trust signals, correction rates, cost per interaction, and value realization
Qualifications Minimum Qualifications
- Bachelor's Degree plus at least 12 years of product management experience, including demonstrated ownership of AI\-enabled or decision\-automation outcomes, and at least 4 years in a product leadership role, or demonstrated equivalent experience and education
- Proven experience owning outcomes for AI\-enabled products, including autonomy decisions, adoption strategy, and measurable business impact
- Strong ability to translate business problems into AI\-ready product definitions, including intents, guardrails, evaluation plans, and human\-AI workflows
- Demonstrated judgment balancing AI value, risk, trust, and cost under real\-world constraints
- Excellent communication and stakeholder influence skills across Product, Responsible AI, Architecture, Data, Operations, and Legal
Preferred Qualifications
- Direct experience defining autonomy levels, HITL policies, and Responsible AI requirements for production systems
- Familiarity with agentic workflows, RAG\-based experiences, AI evaluation frameworks, and observability metrics from a product ownership perspective
- Experience establishing AI success measures such as containment, correction rates, explainability, bias controls, and user trust
- Fluency collaborating with architects and engineers on AI constraints (data readiness, model risk, cost/quality trade\-offs) without owning technical implementation
- Track record driving behavior change and adoption for AI capabilities in regulated, high\-risk, or mission\-critical environments
- Experience operating in federated or matrixed product and delivery models, with strong focus on value realization and continuous learning loops
Additional Information
- Relocation assistance is not available for this position
- Sponsorship for US work authorization is not available for this position, now or in the future.
At AECOM, we are committed to maintaining a secure and trustworthy recruitment process and take any fraudulent hiring activity seriously. *To support this commitment, all newly hired employees are required to attend an in\-person Day 1 onboarding at an AECOM office location as a condition of employment.*
About AECOM
AECOM is proud to offer comprehensive benefits to meet the diverse needs of our employees. Depending on your employment status, AECOM benefits may include medical, dental, vision, life, AD\&D, disability benefits, paid time off, leaves of absences, voluntary benefits, perks, flexible work options, well\-being resources, employee assistance program, business travel insurance, service recognition awards, retirement savings plan, and employee stock purchase plan.
AECOM is the global infrastructure leader, committed to delivering a better world. As a trusted professional services firm powered by deep technical abilities, we solve our clients’ complex challenges in water, environment, energy, transportation and buildings. Our teams partner with public\- and private\-sector clients to create innovative, sustainable and resilient solutions throughout the project lifecycle – from advisory, planning, design and engineering to program and construction management. AECOM is a Fortune 500 firm that had revenue of $16\.1 billion in fiscal year 2025\. Learn more at aecom.com.
What makes AECOM a great place to work
You will be part of a global team that champions your growth and career ambitions. Work on groundbreaking projects \- both in your local community and on a global scale \- that are transforming our industry and shaping the future. With cutting\-edge technology and a network of experts, you’ll have the resources to make a real impact. Our award\-winning training and development programs are designed to expand your technical expertise and leadership skills, helping you build the career you’ve always envisioned. Here, you’ll find a welcoming workplace built on respect, collaboration and community—where you have the freedom to grow in a world of opportunity.
As an Equal Opportunity Employer, we believe in your potential and are here to help you achieve it. All your information will be kept confidential according to EEO guidelines.
Salary Context
This $170K-$220K range is above 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 AECOM, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($195K) sits 9% above the category median. Disclosed range: $170K to $220K.
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.
AECOM AI Hiring
AECOM has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $180K - $220K.
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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