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About This Role
Role Title: AI & Cloud DevOps Engineer
Reports To: Service Area Leader
Employment Type: Full-Time W2
Location: 100% Remote
At RevStar, we are a dynamic team of engineers, UX designers, and product managers committed to putting business first in the pursuit of excellence. As an Advanced Technology Partner, we deliver cloud-native web and mobile applications, building modern data and AI solutions that drive tangible value. We foster a remote-first culture where teams freely innovate and grow together, building meaningful outcomes that matter. Our ethos is guided by Service, Collaboration, Transparency, Accountability, and Quality.
Our Core Values:
- Self-Mastery: We hold a high bar for how we think, communicate, and improve.
- Ownership: We own outcomes, not just effort.
- Shared Destiny: We rise or fall together.
The Opportunity We are looking for a strategic AI & Cloud DevOps Engineer who bridges technical execution with client-facing consulting. In this role, you will architect cloud-native infrastructure, automate enterprise deployments, and collaborate directly with client stakeholders to align cloud and AI strategies with business outcomes. You will thrive in a fast-paced environment where autonomy, technical depth, and clear communication drive everyday success.
Key Pillars of Responsibility* Cloud & AI Architecture & Infrastructure: Architect, deploy, and manage scalable cloud and AI environments utilizing ECS, EKS, Lambda, S3, API Gateway, or AI platform services while ensuring security, reliability, and cost-efficiency.
- Automation & CI/CD Pipelines: Build automated CI/CD pipelines and version-controlled Infrastructure as Code (IaC) using Terraform, GitHub, Azure DevOps, and cloud/AI tooling for seamless releases.
- Observability & Optimization: Implement proactive monitoring, logging, and alerting solutions across multi-cloud and AI infrastructure environments to optimize performance and prevent operational incidents.
- Client Engagement & Delivery: Lead discovery workshops, demo solutions, gather technical requirements, and build immediate credibility with enterprise client stakeholders.
Requirements* 7+ Years in Cloud & DevOps Engineering: Extensive background designing, deploying, and maintaining enterprise cloud infrastructure.
- 5+ Years of Cloud or AI Platform Mastery: Deep hands-on experience across Anthropic, OpenAI, or AWS environments, serverless architectures, and containerized workloads.
- 3+ Years in Consulting or Client-Facing Roles: Proven track record operating as an independent actor—autonomously leading workload assessments, architecting cloud adoption roadmaps, and driving client engagements with minimal oversight.
Technical Execution & Automation* Infrastructure as Code (IaC) & Containers: Advanced expertise in Terraform, container orchestration (such as AWS ECS/EKS or equivalent cloud container services), and automated provisioning.
- CI/CD & DevSecOps: Mastery in building secure, automated pipelines (GitHub Actions, Azure DevOps, or cloud-native tooling) with integrated SAST/DAST, policy-as-code, and automated testing.
- Security & Financial Operations (FinOps): Deep experience in cloud/AI security baselines (IAM least-privilege, guardrails, compliance automation) and cost-optimization frameworks.
Certifications & Communication* Active Certifications: Professional-level Cloud, DevOps, or AI Certifications (e.g., AWS Certified DevOps Engineer – Professional, Solutions Architect – Professional, Specialty certs in Security / AI Practitioner, or equivalent Anthropic / OpenAI / Cloud platform credentials) strongly preferred.
- Consultative Communication: Superior verbal, written, and presentation skills—confident in facilitating technical workshops, demoing complex architectures to stakeholders, and thriving in a 100% remote environment.
Benefits
- Paid Time Off – Take the time you need to recharge and stay productive.
- Remote-First Working Environment – Collaborate from anywhere while staying connected with our global team.
- Comprehensive Health Coverage – Medical, Dental, Vision
- 401(k) Retirement Plan – Plan for your future with access to a company-sponsored 401(k) program.
- Annual Learning & Development Stipend – Invest in your skills with conferences, certifications, or courses.
- Peer Mentorship & Coaching – Learn from experienced engineers, product managers, and architects to accelerate your growth.
- Professional Growth Opportunities – Exposure to cutting-edge AWS GenAI, data, and cloud technologies across diverse industries.
- Company Outings & Volunteer Opportunities – Build relationships and give back to the community.
- Collaborative, Innovative Culture – Work alongside top talent in a fast-paced, supportive environment that values curiosity and initiative.
Equal Opportunity Employment
At RevStar, we don’t just accept differences — we celebrate them, we support them, and we thrive on them for the benefit of our employees, our customers, and our community. RevStar is proud to be an equal opportunity workplace.
Reasonable Accommodations
RevStar is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. To request reasonable accommodation, contact HR at [email protected].
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Revstar, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Revstar AI Hiring
Revstar has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span AZ, US, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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