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About This Role
Hi, Our client is looking for GenAI /Google Cloud Platform Engineer for Charlotte, NC. If you are looking for a job change, please let me know. GenAI /Google Cloud Platform Engineer Charlotte, NC 12\+ Months of Contract Role Job Description:
Key Responsibilities:
- Design, implement, and maintain Infrastructure as Code (IaC) solutions using Terraform to provision and manage Azure resources, including Azure Machine Learning (Azure ML), Azure AI Studio, Azure Kubernetes Service (AKS), Azure Databricks, and related services for ML/ GenAI model deployment.
- Deploy and orchestrate ML and GenAI models in enterprise ML platforms, ensuring end\-to\-end automation from model training to inference, including integration with CI/CD pipelines (e.g., Azure DevOps or GitHub Actions).
- Collaborate with data scientists, ML engineers, and cross\-functional teams to architect multi\-cloud environments (Azure primary, with AWS/Google Cloud Platform integrations), focusing on hybrid deployments, data sovereignty, and disaster recovery.
- Optimize cloud infrastructure for AI/ML workloads, including compute clusters, storage (e.g., Azure Blob Storage, ADLS), networking (e.g., Virtual Networks, Private Endpoints), and security (e.g., Azure RBAC, Key Vault, Sentinel).
- Implement MLOps practices, such as model versioning, monitoring, logging, and alerting using tools like Azure Monitor, Prometheus, or MLflow to ensure reliable production deployments.
- Develop and enforce IaC best practices, including modular Terraform code, state management (e.g., Azure Storage for remote state), drift detection, and automated testing with tools like Terragrunt or Checkov.
- Troubleshoot and resolve infrastructure issues in production AI environments, ensuring high availability, scalability, and compliance with enterprise standards (e.g., GDPR, SOC 2\).
- Conduct code reviews, mentor junior engineers, and contribute to documentation for IaC patterns specific to ML/ GenAI use cases.
- Stay updated on emerging Azure ML services (e.g., Azure OpenAI Service, Prompt Flow) and integrate them into multi\-cloud IaC frameworks.
- Participate in on\-call rotations and incident response for critical AI infrastructure.
Required Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent experience).
- 5\+ years of experience as a Cloud Engineer, DevOps Engineer, or similar role, with at least 3 years focused on Terraform for IaC in Azure environments.
- Proven expertise in deploying ML/ GenAI models using Azure ML services, including model training, registration, endpoints, and inference pipelines.
- Strong hands\-on experience with multi\-cloud architectures (Azure required; AWS/Google Cloud Platform preferred), including cross\-cloud networking, identity federation, and resource orchestration.
- In\-depth knowledge of IaC concepts, including Terraform modules, providers (e.g., AzureRM), variables, outputs, and advanced features like workspaces and backends.
- Solid understanding of Machine Learning lifecycle, including data ingestion, feature engineering, model serving, and scaling in enterprise AI platforms (e.g., Azure ML, SageMaker, Vertex AI).
- Experience with containerization and orchestration tools like Docker, Kubernetes (AKS), and Helm for AI workloads.
- Proficiency in scripting languages such as Python, PowerShell, or Bash for automation.
- Familiarity with security best practices in cloud ML environments, including encryption, access controls, and vulnerability scanning.
- Excellent problem\-solving skills and ability to work in agile teams.
Preferred Qualifications:
- Certifications such as Microsoft Certified: Azure DevOps Engineer Expert, Azure AI Engineer Associate, or HashiCorp Certified: Terraform Associate.
- Experience with additional IaC tools like ARM Templates, Bicep, or Pulumi for hybrid Azure setups.
- Background in MLOps tools like Kubeflow, MLflow, or Azure ML Pipelines for enterprise\-scale deployments.
- Knowledge of cost optimization in cloud AI environments using tools like Azure Cost Management.
- Prior experience in regulated industries (e.g., finance, healthcare) with compliance\-focused IaC.
For applications and inquiries, contact: hirings@openkyber.com
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Openkyber, 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 $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Openkyber AI Hiring
Openkyber has 161 open AI roles right now. They're hiring across AI/ML Engineer, AI Consultant, AI Engineering Manager, MLOps Engineer. Positions span GA, US, NJ, US, IL, US. Compensation range: $120K - $199K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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