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About This Role
In a world of possibilities, pursue one with endless opportunities. Imagine Next!
At Parsons, you can imagine a career where you thrive, work with exceptional people, and be yourself. Guided by our leadership vision of valuing people, embracing agility, and fostering growth, we cultivate an innovative culture that empowers you to achieve your full potential. Unleash your talent and redefine what’s possible. Job Description:
Parsons X is seeking an experienced Lead for the successful delivery of digital transformation initiatives supporting complex infrastructure programs. This position is embedded onsite with a strategic client and is responsible for managing project execution while driving the adoption of modern construction technologies, AI\-enabled solutions, and digital delivery best practices.
In addition to leading one major digital transformation engagement, this role will support project management, client engagement, and innovation initiatives across additional infrastructure programs.
The ideal candidate combines strong project management fundamentals with a passion for emerging technologies and a practical understanding of how AI can improve construction project delivery, collaboration, reporting, and decision\-making.
What You'll Be Doing:
Project Management
- Serve as the onsite Project Manager responsible for day\-to\-day delivery of a digital transformation program.
- Manage project scope, schedule, budget, risks, deliverables, and resource planning.
- Coordinate multidisciplinary teams delivering BIM, digital engineering, PMIS, analytics, and technology initiatives.
- Facilitate client meetings, project reviews, and executive status updates.
- Track project performance and ensure contractual commitments are achieved.
- Identify project risks and implement mitigation strategies.
- Support overall program management activities across multiple infrastructure projects.
AI Innovation \& Digital Delivery
- Identify opportunities to leverage Artificial Intelligence to improve project delivery and operational efficiency.
- Evaluate and implement AI tools that enhance reporting, document management, knowledge sharing, and project controls.
- Work with technical teams to integrate AI capabilities into digital workflows and construction management processes.
- Promote adoption of AI technologies through pilot projects, demonstrations, and user engagement.
- Stay informed on emerging AI capabilities relevant to the architecture, engineering, and construction (AEC) industry.
- Collaborate with stakeholders to identify practical, business\-focused AI use cases.
Client Engagement
- Build strong working relationships with client stakeholders and project teams.
- Serve as a trusted advisor for digital delivery and construction technology initiatives.
- Support workshops, presentations, and demonstrations of digital solutions.
- Assist with identifying opportunities to expand digital services and innovation initiatives.
- Represent the organization professionally while working onsite with the client.
Education and Typical Experience
- Bachelor's Degree in Architecture, Engineering, or Construction
- Master's Degree preferred
- 15\+ years related experience, including supervisory/managerial experience
- Experience managing digital transformation or technology implementation projects.
- Strong understanding of construction project delivery methodologies.
- Experience working directly with owners, consultants, contractors, and executive stakeholders.
- Excellent communication, facilitation, and organizational skills.
- Ability to manage multiple priorities in a fast\-paced, client\-facing environment.
What Desired Skills You'll Bring:
Construction Technology
- Building Information Modeling (BIM)
- Autodesk Construction Cloud / BIM 360
- Kahua, Procore, Trimble Unity Construct (e\-Builder), PMWeb or similar PMIS platforms
- Common Data Environments (CDE)
- Digital Twins
- GIS integration
- Reality Capture
AI \& Data
- Azure AI Services
- AI platforms
- Power BI
- Power Automate
- Intelligent document processing
- Workflow automation
- Data analytics and dashboard developme
Security Clearance Requirement:
None
This position is part of our Critical Infrastructure team.
For more than 80 years, our experts have designed and delivered the critical infrastructure that connects and protects communities around the world. We work in collaborative teams, both within the company and with our partners and customers, to plan, design, build, and modernize infrastructure. We take special pride in projects and solutions that improve communities as well as people’s quality of life by promoting economic growth, enhancing mobility, and increasing sustainability and resiliency. Powered by our people, we provide the imagination necessary to support our customers’ visions—and to help them see what's next!
Salary Range: $134,100\.00 \- $241,400\.00
We value our employees and want our employees to take care of their overall wellbeing, which is why we offer best\-in\-class benefits such as medical, dental, vision, paid time off, Employee Stock Ownership Plan (ESOP), 401(k), life insurance, flexible work schedules, and holidays to fit your busy lifestyle!
This position will be posted for a minimum of 3 days and will continue to be posted for an average of 30 days until a qualified applicant is selected or the position has been cancelled.
Parsons is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status or any other protected status.
We truly invest and care about our employee’s wellbeing and provide endless growth opportunities as the sky is the limit, so aim for the stars! Imagine next and join the Parsons quest—APPLY TODAY!
Parsons is aware of fraudulent recruitment practices. To learn more about recruitment fraud and how to report it, please refer to https://www.parsons.com/fraudulent\-recruitment/.
COMPETITIVE BENEFIT OFFERINGS
Financial Wellness
We care about your financial wellbeing. Parsons offers competitive pay and retirement plans to help you build wealth for the future while giving you the flexibility to diversify your investments.
Work Life Harmony
Balance in life is important and time away from the office is imperative to allow you to refresh and focus your attention on the things that matter to you. Parsons supports your time away by providing paid time off and paid flexible holidays.
Career Development
We are committed to fostering the personal and professional growth of our employees. Develop and advance yourself though our comprehensive training, educational and mentorship programs.
Veteran Support
We provide Industry leading benefits to support veterans and active\-duty members to provide security for you and your family by offering robust leave and benefits; including paid active\-duty military leave and paid time off when transitioning back to civilian life.
Mind \& Body
At Parsons we inspire healthier habits, heathier minds, and a healthier you through our wellness program. Participate in our weekly Meditation Mondays and Wellness Wednesdays. Wellness, at Parsons, is more than just your annual checkup.
Health
Health is not a one size fits all. At Parsons, we offer a robust Employee Assistance Program as well as comprehensive medical, dental and vision plans through large, national carriers with the choice of regional PPO, HDHP, or HMO networks.
Salary Context
This $134K-$241K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Parsons, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($187K) sits 13% below the category median. Disclosed range: $134K to $241K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Parsons AI Hiring
Parsons has 8 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Data Scientist, MLOps Engineer. Positions span Baltimore, MD, US, Remote, US, Annapolis Junction, MD, US. Compensation range: $181K - $266K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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