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About This Role
Associate Cloud AI Engineer (Entry Level)Location: Various U.S. Federal Client Sites (Onsite)
Travel: Travel required to customer sites throughout the Continental U.S.
Salary: $80,000 – $100,000
Clearance: Active TS/SCI or the ability to obtain a TS/SCI clearance required
About PGTEK
PGTEK is a leading provider of enterprise IT modernization, cloud transformation, and AI solutions supporting U.S. Federal Government agencies. As we continue expanding our Google Cloud and AI practice, we're seeking motivated engineers who want to build their careers deploying next\-generation cloud and AI technologies in secure government environments.
This is an excellent opportunity for early\-career cloud professionals, recent graduates, military veterans, and PGTEK Academy candidates looking to gain hands\-on experience with Google Cloud Platform (GCP), enterprise AI, and federal cloud modernization projects.
Position Summary
As an Associate Cloud AI Engineer, you'll work alongside Senior and Lead Cloud AI Engineers to help deploy, secure, and support Google Cloud environments for federal customers. You'll participate in cloud migrations, infrastructure deployments, automation initiatives, and AI platform implementations while developing expertise in Google Cloud technologies and federal security best practices.
This role offers significant mentorship and professional growth opportunities as you build toward becoming a Cloud AI Engineer and ultimately a Cloud Architect.
Responsibilities* Assist with deploying and configuring Google Cloud Platform (GCP) environments
- Configure resource hierarchies, IAM roles, Cloud Identity, and organizational policies
- Support Google Cloud Assured Workloads implementations
- Deploy and manage Compute Engine virtual machines, Google Kubernetes Engine (GKE), Cloud Run, and enterprise AI services
- Configure cloud networking including VPCs, subnets, firewall rules, and network security policies
- Assist with Google Cloud storage, databases, and data services including:
- + Cloud Storage
+ Cloud SQL
+ BigQuery
+ Spanner
+ Pub/Sub
+ Dataflow
- Support Infrastructure as Code (IaC) deployments using Terraform, Config Connector, and Helm
- Utilize Google AI\-assisted tools including Gemini CLI and Gemini Cloud Assist
- Monitor cloud environments using Cloud Monitoring, Cloud Logging, Cloud Trace, and Cloud Profiler
- Assist with identity, access management, encryption, and least\-privilege security implementations
- Support compliance documentation and evidence collection for FedRAMP and DoD environments
- Collaborate with senior engineers throughout cloud migration and modernization projects
Continuously expand Google Cloud knowledge through certifications, mentoring, and hands\-on experience
*
Requirements
- Bachelor's degree in Computer Science, Information Technology, Engineering, or another STEM\-related field (or equivalent practical experience)
- 0–2 years of experience supporting cloud infrastructure or enterprise IT environments
- Basic understanding of cloud computing concepts
- Familiarity with Linux and networking fundamentals
- Experience using command\-line tools and scripting is a plus
- Strong troubleshooting and analytical skills
- Excellent communication and willingness to learn
- Ability to work collaboratively in a fast\-paced team environment
Preferred Qualifications
Experience or exposure to any of the following is a plus:
- Google Cloud Platform (GCP)
- Kubernetes
- Docker or container technologies
- Terraform or Infrastructure as Code
- Git and CI/CD pipelines
- Cloud networking
- Python, Bash, or PowerShell scripting
- AI or machine learning concepts
- Legacy\-to\-cloud migration projects
Required Certifications* Google Cloud Certified – Associate Cloud Engineer (ACE) *(Required at hire or within the first 90 days of employment)*
- Google Cloud Certified – Professional Cloud Architect (PCA) *(Preferred or active pursuit encouraged)*
Security Clearance* Active TS/SCI clearance or eligibility to obtain a TS/SCI clearance is required.
Benefits
Our comprehensive benefits package for full\-time salaried employees is effective immediately upon the start date. Benefits include comprehensive PPO medical coverage with access to a Health Savings Account (HSA) option, a vision plan, and dental insurance with the base dental plan option paid for by PGTEK. Life Insurance, Short and Long\-Term disability, and Critical Illness insurance have premiums covered. Additionally, PGTEK offers a matching 401(k) plan and a discount on pet insurance through ASPCA Pet Insurance. An Employee Assistance Program is available at no cost to all employees. PGTEK offers a generous amount of PTO and Holidays, and an Education Assistance Program is available after 12 months of employment.
ABOUT PGTEK
PGTEK is a true consulting organization dedicated to helping clients achieve their business and technology objectives utilizing our decades of experience and business relationships. PGTEK invests in the educational advancements of our staff by providing the necessary resources to complete Professional and Business Certifications. Our company is our people, and we treat them like family.
EOE, including disability/veterans
Salary Context
This $80K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PGTEK, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($90K) sits 59% below the category median. Disclosed range: $80K to $100K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
PGTEK AI Hiring
PGTEK has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $100K - $150K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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