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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 is looking for an Applied AI Engineer to help build production-grade AI applications that transform complex real-world data into actionable information.
This role sits at the intersection of applied AI, computer vision, robotics, geospatial systems, and software engineering. You will work with diverse data sources, including drone imagery and video, mobile and 360° imagery, point clouds, LiDAR, geospatial data, and 3D models, and develop systems that apply modern AI techniques to analyze, organize, and reason over that information.
Our work increasingly touches drones, intelligent sensing, and autonomy, particularly where AI can improve how real-world environments are captured, reconstructed, understood, and inspected.
This is a hands-on engineering role for someone who enjoys taking ideas from prototype to production. You should be comfortable working across AI models, backend services, data pipelines, and user-facing applications.
This role would be based out of Parsons Westminster, Colorado office and will require in-office presence.
What You'll Do
- Design and build AI-enabled applications from prototype through production.
- Develop computer vision and multimodal AI workflows for imagery, video, drone data, geospatial data, and 3D environments.
- Apply modern techniques including segmentation, feature extraction, self-supervised learning, foundation models, LLMs, and VLMs.
- Develop AI workflows supporting drone-based inspection, mapping, perception, and emerging autonomous capabilities.
- Develop scalable Python services and APIs using frameworks such as FastAPI.
- Build data pipelines integrating AI inference, SQL databases, APIs, sensor-derived data, and external data sources.
- Optimize and deploy AI models using PyTorch, ONNX, and related inference tooling.
- Collaborate with engineers and domain experts to turn loosely defined technical problems into usable software.
- Contribute to software architecture, testing, source control, automated deployment, and production reliability.
- Rapidly evaluate emerging AI techniques and determine where they provide practical value.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Robotics, Data Science, or a related technical discipline, or equivalent practical work experience.
- 2+ years of experience building production-grade software using Python.
- Strong software engineering fundamentals, including modular design, testing, debugging, and maintainable code.
- Experience developing and integrating machine-learning or computer-vision models into real applications.
- Experience with PyTorch or a comparable ML framework.
- Experience building APIs and backend services; FastAPI or similar Python frameworks preferred.
- Experience working with SQL databases and structured data.
- Familiarity with modern AI techniques such as computer vision foundation models, embeddings, LLMs, or VLMs.
- Proficiency with Git/source control, Linux development environments, and CI/CD workflows.
- Ability to independently prototype, evaluate, and productionize new technical approaches.
Preferred Qualifications
- Experience with TypeScript and React or similar modern web-development frameworks.
- Experience building full-stack AI applications.
- Experience with geospatial or 3D data, including one or more of: LiDAR and point cloudsPhotogrammetryGIS/geospatial imageryDigital twins or facility modelsCamera geometry and 3D reconstruction
- Experience working with drone platforms, aerial imagery, mapping, or autonomous sensing systems.
- Experience with ONNX, GPU inference, model optimization, or edge deployment.
- Experience developing applications using LLMs or VLMs or SSL methods (SAM, DINO v3)
- Experience with segmentation, feature matching, embedding models, or self-supervised computer vision.
- Familiarity with Docker, cloud deployment, automated testing, and modern DevOps practices.
- Familiarity with ROS/ROS 2, localization, sensor systems, or robotics software.
- Experience working with drone, mobile, camera, or other real-world sensor datasets.
What We're Looking For
We are looking for a builder: someone who can understand a technical problem, explore modern AI approaches, implement the supporting software, and deliver something people can actually use.
The ideal candidate is comfortable moving between AI experimentation and production engineering and is excited by problems involving computer vision, drones, spatial data, intelligent sensing, and real-world infrastructure.
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: $74,800.00 - $130,900.00 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.
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Salary Context
This $74K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($102K) sits 51% below the category median. Disclosed range: $74K to $130K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Parsons AI Hiring
Parsons has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span CO, US, FL, US, MD, US. Compensation range: $130K - $266K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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