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About This Role
About Premier Cloud
Premier Cloud is a Google Cloud Premier Partner dedicated to helping organizations modernize, collaborate, and scale through the power of cloud technology. Founded in 2001, we deliver cloud consulting, managed services, and tailored solutions to SMB and enterprise clients across North America.
As one of Canada’s fastest\-growing companies, we help businesses unlock the full potential of Google Cloud, along with leading SaaS platforms like JumpCloud, Lumin, and Virtru. Our team is driven by innovation, technical excellence, and a shared commitment to delivering measurable impact for our clients.
At Premier Cloud, we take pride in maintaining a collaborative, light\-hearted culture that values creativity, growth, and inclusion. Year after year, we’re recognized as a Great Place to Work\-Certified™ organization and featured among the Best Workplaces in Technology.
Join as a Gen AI Solutions Engineer — Premier Cloud
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Location: Hybrid remote, Austin, TX 78701 Job Type: Full\-time Travel: Up to 30% (customer sites, Google offices, industry events)
About Premier Cloud
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As a Google Cloud Premier Partner, Premier Cloud helps SMB and Enterprise clients across North America modernize and innovate through cloud\-native solutions, specialized consulting, and managed services.
- Recognized as one of Canada’s fastest\-growing companies with offices in Victoria, BC, and Austin, TX.
- Certified as a "Great Place to Work" for six consecutive years.
- Expertise spans Google Workspace migrations, AI/Data infrastructure, and strategic cloud consulting.
The Role \& Core Responsibilities
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As a Gen AI Solutions Engineer, you will lead the architecture, design, and delivery of enterprise\-scale Generative AI solutions on Google Cloud Platform. You will run technical discovery with customer teams, design agentic workflows on Vertex AI, and act as a trusted advisor to both engineers and executives — moving fast from whiteboard concepts to production\-grade MVPs. This role requires a strong combination of cloud architecture, MLOps, and hands\-on experience deploying scalable AI workloads.
- Design and deploy agents using Agent Development Kit (ADK), Model Context Protocol (MCP), and Agent\-to\-Agent (A2A) protocols.
- Build production systems with Vertex AI Agent Builder, LangChain, and LlamaIndex
- Architect end\-to\-end agentic workflows from concept through customer deployment
- Architect end\-to\-end multi\-agent systems and automated task assistants using Vertex AI Agent Builder, LangChain, or LlamaIndex.
- Build scalable Retrieval\-Augmented Generation (RAG) pipelines, configure semantic search, and integrate with vector databases.
- Lead client workshops to map out high\-impact, narrow use cases that show fast return on investment (ROI)
- Embed role\-based access, prompt safeguards, and data privacy controls directly into AI models from day one.
- Run discovery workshops with customer leadership to define objectives, constraints, and success metrics, delivering MVPs in weeks.
- Serve as the primary technical point of contact for enterprise accounts, educating stakeholders on AI capabilities and limitations to drive adoption.
Qualifications
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- 4\+ years designing and deploying AI/ML solutions, ideally in a customer\-facing or consulting role
- Hands\-on experience building agents and agentic workflows with modern frameworks (LangChain, LlamaIndex, ADK)
- Strong Python skills and experience with ML frameworks (TensorFlow, PyTorch, scikit\-learn, Hugging Face)
- Practical experience with LLM applications, RAG pipelines, vector embeddings, and prompt engineering
- Working knowledge of Google Cloud Platform, particularly Vertex AI (Agent Builder, Model Garden), BigQuery, and Cloud Run
- Strong presentation skills across technical and executive audiences
- Experience with data preparation and feature engineering for production AI systems
- A track record of translating AI capabilities into business strategy and building relationships with customer leadership
Preferred
- Google Cloud Professional Machine Learning Engineer or Data Engineer certification (or willingness to earn one within 6 months)
- Experience supporting sales calls or writing statements of work
- MLOps experience: Docker, Kubernetes, CI/CD pipelines
- Background in consulting or professional services with distributed/remote teams
- Vertex AI Agent Builder, ADK, Gemini APIs, LangChain, LlamaIndex, MCP, A2A
- ML tooling: Python, TensorFlow, PyTorch, Hugging Face Transformers, RAG pipelines, vector databases
- BigQuery, Dataflow, Cloud Run, GKE, Pub/Sub, Cloud Functions
- DevOps: Docker, Kubernetes, GitHub Actions, and Vertex AI Pipelines.
Compensation \& Benefits
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- Health, dental, and vision insurance
- Paid time off
- Ongoing training and certification support
Our Commitment to Inclusion
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Premier Cloud is an equal\-opportunity employer. We value diverse backgrounds and perspectives, and we encourage you to apply even if you don't meet every qualification listed
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 Premier Cloud, 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.
Premier Cloud AI Hiring
Premier Cloud has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 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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