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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 amazingly talented AI Engineer to join our team! In this role you will get to provide technical support for the design, evaluation, and advancement of AI\-enabled applications, tools, and workflows. This role is intended for a hands\-on technical professional who understands how modern AI systems are built and can help develop practical solutions using large language models, agentic workflows, orchestration frameworks, and supporting software infrastructure. The right candidate is a builder at heart—someone who explores new frameworks, prototypes ideas, and continuously teaches themselves emerging techniques. Curiosity and passion matter greatly in this role. We want someone who follows the fast\-moving AI ecosystem because they genuinely enjoy it, not just because the job requires it.
What You'll Be Doing:
- Provide SETA support to government teams developing or assessing AI\-enabled applications and tools.
- Advise on technical approaches for AI application design, prototyping, integration, and evaluation.
- Assess solutions involving LLMs, retrieval\-augmented generation, tool use, agentic workflows, orchestration layers, prompt pipelines, and evaluation harnesses.
- Support the review of architectures, development approaches, model usage patterns, and technical risk areas.
- Advise on AI engineering considerations such as latency, reliability, observability, testability, guardrails, data flow, and system integration.
- Evaluate and compare modern AI frameworks, agentic libraries, and experimentation harnesses for suitability in mission use cases.
- Assist with the development of technical guidance, prototypes, engineering recommendations, and briefings for government stakeholders.
- Support test and evaluation activities for AI tools, including performance assessment, qualitative review, and workflow validation.
- Collaborate with platform engineers, product leads, mission users, and government leadership to ensure AI efforts remain technically grounded and operationally relevant.
- Stay current on rapid changes in the AI ecosystem and provide informed recommendations on emerging tools and practices.
What Required Skills You'll Bring:
- Masters degree in Computer Science, Engineering, Data Science, Mathematics, or related field and 12\+ years of relevant experience
- Active Secret clearance required
- Experience designing, building, integrating, or evaluating AI/ML\-enabled applications or software tools.
- Familiarity with modern AI application patterns, including LLM\-based applications, RAG, prompt engineering, agentic systems, and AI\-assisted workflows.
- Experience with software development fundamentals, APIs, data handling, and system integration.
- Ability to assess technical tradeoffs and communicate them clearly to both engineers and government stakeholders.
- Strong written and verbal communication skills.
What Desired Skills You'll Bring:
- Ability to obtain a TS SCI clearance required
- Experience with Python, JavaScript/TypeScript, or similar languages commonly used in AI application development.
- Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses.
- Exposure to model hosting, inference pipelines, or cloud\-based AI development environments.
- Understanding of secure AI development practices, data protection, and operational constraints in government settings.
- Experience supporting defense, cyber, intelligence, or mission application development.
- Familiarity with human\-in\-the\-loop workflows, AI assurance, and evaluation methodologies.
- Working knowledge of one or more agentic or orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, Haystack, LlamaIndex, or similar ecosystems.
Security Clearance Requirement:
An active Secret security clearance is required for this position.
This position is part of our Federal Solutions team.
The Federal Solutions segment delivers resources to our US government customers that ensure the success of missions around the globe. Our intelligent employees drive the state of the art as they provide services and solutions in the areas of defense, security, intelligence, infrastructure, and environmental. We promote a culture of excellence and close\-knit teams that take pride in delivering, protecting, and sustaining our nation's most critical assets, from Earth to cyberspace. Throughout the company, our people are anticipating what’s next to deliver the solutions our customers need now.
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!
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: $180K across 2064 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 3,963 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 $180,000 based on 12,398 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. Disclosed range: $134K to $241K.
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 ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Parsons AI Hiring
Parsons has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Huntsville, AL, US, Washington, DC, US, Remote, US. Compensation range: $111K - $332K.
Location Context
Across all AI roles, 15% (593 positions) offer remote work, while 3,349 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,103 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 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 ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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