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About This Role
Senior AI Infrastructure Engineer
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Location: Annapolis Junction, MD
Clearance: TS/SCI with Polygraph required
Work Type: On\-site
Salary: $293,000\-$306,000
Position Overview
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We are seeking an experienced Senior AI Infrastructure Engineer to support the design, deployment, and operation of enterprise artificial intelligence and machine learning platforms. This role will be responsible for developing and maintaining scalable infrastructure that enables the delivery of AI\-powered applications and services across the organization.
The successful candidate will independently design, implement, and operate cloud\-native infrastructure components while supporting modern AI technologies, distributed systems, and production service environments. This position requires strong expertise in platform engineering, cloud technologies, automation, observability, and software development.
Key Responsibilities
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- Design, implement, and optimize infrastructure supporting AI model deployment and inference at scale.
- Develop, maintain, and support production AI services and applications.
- Collaborate with stakeholders and engineering teams to define technical solutions for evolving business and operational requirements.
- Design and implement scalable, reliable, and maintainable platform architectures.
- Drive adoption of emerging technologies, engineering best practices, and automation solutions.
- Implement monitoring, logging, alerting, and observability capabilities for platform services.
- Automate infrastructure provisioning, configuration, and lifecycle management using Infrastructure\-as\-Code (IaC) methodologies.
- Ensure high availability, reliability, performance, and scalability of platform services.
- Support the secure deployment and operation of AI systems and associated data environments.
- Contribute to system architecture reviews, platform modernization efforts, and operational support activities.
- Provide technical guidance, knowledge sharing, and mentorship to engineering team members.
- Participate in troubleshooting, root cause analysis, and continuous improvement initiatives.
Required Qualifications
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### Education and Experience
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, Computer Engineering, or a related technical discipline and eight (8\) years of relevant experience; OR
- Four (4\) additional years of directly related experience may be substituted for the degree requirement.
### Technical Qualifications
- Demonstrated experience building, deploying, and maintaining enterprise\-scale production systems.
- Experience designing and supporting high\-volume web applications and distributed service architectures.
- Strong background in systems integration across diverse technologies, platforms, and cloud environments.
- Hands\-on experience designing, deploying, and operating cloud infrastructure in Amazon Web Services (AWS).
- Experience administering and deploying applications using Kubernetes.
- Strong software development skills using Python.
- Experience implementing observability and monitoring solutions using technologies such as:
+ Application Performance Monitoring (APM) tools
+ OpenTelemetry
+ Grafana
+ Prometheus
- Experience developing and maintaining Continuous Integration and Continuous Deployment (CI/CD) pipelines.
- Knowledge of DevOps principles, automation practices, and modern software delivery methodologies.
- Demonstrated ability to lead technical initiatives and influence engineering practices across teams.
- Ability to operate effectively in dynamic environments with evolving requirements.
- Excellent written and verbal communication skills.
Preferred Qualifications
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- Experience supporting AI model deployment, serving, and inference platforms.
- Experience integrating generative AI and large language model (LLM) technologies into enterprise applications.
- Experience with AI workflow orchestration frameworks, including LangChain or similar technologies.
- Knowledge of vector databases, embedding technologies, and semantic search solutions.
- Experience implementing Retrieval\-Augmented Generation (RAG) architectures.
- Experience with distributed computing, high\-performance computing, or large\-scale processing environments.
- Familiarity with autonomous agent frameworks and emerging AI technologies.
Knowledge, Skills, and Abilities
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- Strong cloud engineering and platform architecture expertise.
- Deep understanding of distributed systems and cloud\-native application design.
- Ability to balance reliability, security, scalability, and performance requirements.
- Strong analytical and problem\-solving skills.
- Ability to lead technical initiatives and influence organizational technology adoption.
- Strong collaboration and stakeholder engagement skills.
- Excellent organizational skills and attention to detail.
- Ability to mentor engineers and contribute to a culture of technical excellence.
Benefits
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This position includes a competitive and flexible benefits package, including:
- Medical
Employer pays 100% of the monthly premium for the employee and 80% for the employee’s dependents.
- Health Savings Account (HSA)
Save for all medical, dental, vision and prescription expenses by contributing pre\-tax money to an HSA account. Employer contributes 50% of the annual deductible (prorated to start date).
- Dental and Vision
Employer pays 100% of the monthly premium for the employee and 80% for dependents.
- Life Insurance
100% company\-paid Life and Accidental Death \& Dismemberment (AD\&D) coverage offered to all full\-time employees.
- Short\-Term Disability
100% company\-paid short\-term disability. This benefit pays out 60% of earnings, with a $1,500 maximum for up to 12 weeks.
- Retirement Plan
Automatic 6% of salary contributed to the company 401(k) plan, fully vested. Employee match encouraged but not required.
- Paid Time Off (PTO) \& Holidays
5–6 weeks of PTO based on tenure with the company, in addition to 11 paid holidays.
- Tuition Reimbursement
$5,000 annually for courses directly related to job role and responsibilities.
- Training Reimbursement
Paid training, certification courses, and conferences to support employee career growth.
We do not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non\-merit factor.
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Salary Context
This $293K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Staffed4U, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($299K) sits 62% above the category median. Disclosed range: $293K to $306K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Staffed4U AI Hiring
Staffed4U has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Annapolis Junction, MD, US. Compensation range: $198K - $306K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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