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About This Role
Lead Cloud AI Engineer (Lead / Senior)Location: Various U.S. Federal Client Sites (Onsite)
Travel: Yes – Travel to federal client sites as required
Salary: $120,000 – $150,000
Clearance: Active TS/SCI
About PGTEK
PGTEK is a leading provider of enterprise IT modernization, cloud transformation, and infrastructure solutions supporting U.S. Federal Government agencies. As we expand our Google Cloud and AI practice, we're seeking a Lead Cloud AI Engineer to serve as the senior technical authority for enterprise cloud migration and AI platform modernization initiatives.
This is a highly visible leadership role responsible for architecting secure, compliant Google Cloud environments while mentoring engineering teams and guiding customers through large\-scale cloud transformation efforts.
Position Summary
As the Lead Cloud AI Engineer, you will own the technical strategy, architecture, and execution of Google Cloud and Gemini for Government deployments. You'll work directly with federal customers to design secure cloud solutions, lead complex migration initiatives, establish architectural standards, and ensure every deployment aligns with FedRAMP, DoD, and NIST compliance requirements.
This role combines hands\-on technical leadership with customer engagement, mentoring, and enterprise architecture.
Responsibilities* Lead architecture and design for enterprise Google Cloud Platform (GCP) environments
- Develop migration strategies for legacy, hybrid, and multi\-cloud infrastructures
- Design secure cloud architectures using Google Cloud Assured Workloads
- Establish governance and architectural standards based on Google Cloud Well\-Architected Framework
- Serve as the primary technical advisor to federal customers and agency leadership
- Lead cloud modernization initiatives from planning through implementation
- Design secure IAM, resource hierarchy, networking, and encryption strategies
- Architect solutions utilizing Cloud KMS, CMEK, VPC Service Controls, Organization Policies, and hierarchical firewalls
- Drive FedRAMP authorization and Authority to Operate (ATO) efforts alongside compliance teams and 3PAOs
- Lead infrastructure automation, CI/CD, deployment governance, and operational excellence initiatives
- Oversee observability, monitoring, release management, penetration testing, and continuous compliance
- Guide implementation of Gemini for Government, Google AI APIs, Model Garden, and enterprise AI solutions
- Mentor Associate and Mid\-Level Cloud Engineers while helping develop the organization's Google Cloud practice
- Provide technical leadership throughout the full project lifecycle including planning, deployment, optimization, and ongoing support
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, or a related STEM discipline
- 5–8\+ years of experience designing and leading enterprise cloud solutions
- Extensive experience architecting and deploying Google Cloud Platform (GCP) environments
- Strong background leading enterprise cloud migration initiatives
- Deep understanding of Google Cloud Professional Cloud Architect domains
- Experience implementing Google Cloud Well\-Architected Framework best practices
- Strong knowledge of NIST SP 800\-53, RMF, and federal security compliance
- Experience leading ATO efforts and working with federal agencies
- Proven leadership experience managing technical teams and customer engagements
- Excellent communication and presentation skills
Preferred Qualifications* Experience supporting FedRAMP High, DoD IL4, IL5, or IL6 environments
- Experience with Gemini for Government and enterprise AI platforms
- Knowledge of Google AI APIs, Agent Builder, and Model Garden
- Experience with AI Hypercomputer, GPU, or TPU environments
- Familiarity with Google Distributed Cloud (GDC) air\-gapped environments
- Strong software development lifecycle (SDLC) and DevSecOps experience
- Experience with enterprise API management (Apigee)
- Background in cloud financial management and migration planning
Required Certifications* Google Cloud Certified – Professional Cloud Architect (PCA) *(Required at time of hire)*
- Google Cloud Certified – Associate Cloud Engineer (ACE) *(Required or expected foundational certification)*
Security Clearance* Active TS/SCI clearance \- or eligibility to get one (meaning at least an active Top Secret).
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 $120K-$150K 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($135K) sits 38% below the category median. Disclosed range: $120K to $150K.
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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