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About This Role
Cloud/AI Delivery Manager
Promevo, LLC was founded in 2001 by a group of experienced systems integration, application development, and systems administration specialists in Cincinnati, OH.
We are Promevo!
The name Promevo is derived from the Latin words promoveo, meaning "to move ahead" and promereo, meaning "to earn". It’s our name, but it’s also our purpose. We move clients ahead in technology, enabling their competitive advantage and we earn their trust with our technical excellence and dedication to their success. We believe that digital solutions shouldn’t be something our clients need to adapt to, instead we make digital solutions adapt to client needs.
Promevo is a Google Premier Partner for Google Workspace, Google Cloud, and Google Chrome, specializing in helping businesses harness the power of Google and the opportunities of AI. From technical support and implementation to expert consulting and custom solutions like gPanel, we empower organizations to optimize operations and accelerate growth in the AI era.
In addition to Google technology, Promevo's SaaS Platform (gPanel) provides our clients with a centralized user management interface providing administrators with visibility and control over all of their users' data and settings with its robust suite of security features. We walk alongside our clients to help them achieve their digital transformation and infrastructure goals by providing Professional Services such as migrations, custom app development, fast track deployments, and white glove services.
This is an exciting time to be part of Promevo as we are growing rapidly. We are seeking an experienced Cloud/AI Delivery Manager (Project Manager) with a strong background in Agile methodology to join our team. The ideal candidate will have a track record of successfully delivering complex IT projects on time, within budget, and meeting stakeholder expectations. Familiarity with Google Cloud Platform is a plus, as well as HIPAA and other compliance knowledge.
By joining the Promevo Team, you will find that our company culture is at the heart of our success. Promevo’s core values center around building trust, investing in care of each other, owning our work, respecting our differences, and engaging in positive attitudes. By living out these values each day, we build supportive teams that nurture development and growth, both individually and professionally.
Our Promevo team members are committed to living out the core values below:
- Build and Extend Trust – Show respect and listen. Do what you say you will do. Be transparent and dependable.
- Keep it Human – Invest in relationships. Show empathy. Do right by others. Take care of yourself, your family, and each other.
- Own It – Become an expert in your role. Own the outcome. Finish what you start. Answer for the results.
- Differences Make Us Stronger – Respect Differences. Seek to include different perspectives. Learn from each other.
- Attitude is Contagious – Be fully engaged. Take pride in what you do. Spread your positivity. Courage. Energy. Passion. Purpose.
Duties and Responsibilities
- Manage end\-to\-end IT project delivery (specifically managing cloud delivery projects in Google Cloud), including planning, scoping, budgeting, resource allocation, risk management, and stakeholder management.
- Act as a liaison between clients, technical delivery teams and other organizational units to ensure clear communication, alignment on objectives, and shared success metrics.
- Collaborate with cross\-functional teams, including developers, designers, architects, testers, and other stakeholders to ensure timely delivery of projects.
- Collaborate with Sales, Client Success and Technology Leadership to establish and implement best practices that enhance project efficiency and customer satisfaction
- Communicate effectively with clients, technical teams and executive leadership, translating complex technical details into achievable project deliverables and timelines.
- Ensure that projects are delivered within scope, budget, and timeline constraints while meeting quality standards and stakeholder expectations.
- Implement project management methodology, appropriate to the project, to enable fast delivery, high quality, and excellent stakeholder engagement.
- Provide regular project status updates to stakeholders, including senior management and other project sponsors.
- Ensure that project delivery is aligned with organizational goals and objectives.
- Identify and deliver process improvements, best practices, and automation opportunities to enable excellent project delivery.
- Become Google certified as a Digital Cloud Leader within the first 60 days of hire
Skills and Qualifications
- Bachelor's degree
- 7\+ years of experience in IT project management, required
- Familiarity with Google technologies is required
- Experience leading technology projects leveraging Google Cloud \& Google Workspace
- Strong experience with project management methodologies (including Agile)
- Excellent communication and interpersonal skills, including the ability to work collaboratively with cross\-functional teams.
- Strong organizational skills, including the ability to manage multiple projects and priorities.
- Strong leadership skills, including the ability to motivate and lead a team to achieve project goals.
- Familiarity with project management tools, such as Jira, Asana, or Trello.
What Promevo Offers
- Competitive salary and bonus plan
- 14 Paid holidays
- Medical Insurance
- Dental Insurance, 100% company paid premiums
- Vision Insurance , 100% company paid premiums
- Fully Company Paid Short term Disability Insurance
- Fully Company Paid Long Term Disability Insurance
- Fully Company Paid Life Insurance and AD\&D
- 401k plan: Promevo offers a Safe Harbor 401K plan for full time employees with immediate eligibility. Promevo matches the first 3% of earnings you contribute at 100% and matches the next 2% of earnings you contribute at 50%
- Home office setup allowance
- Cell phone allowance
- 4 weeks of PTO and a healthy work/life balance
- Development and Training opportunities to help you grow
*Promevo LLC is an Equal Opportunity Employer and does not discriminate on the basis of race or ethnicity, religion, sex, national origin, age, veteran disability or genetic information or any other reason prohibited by law in employment.*
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Promevo, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Promevo AI Hiring
Promevo has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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