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About the Role
As a Gen AI Solutions Engineer, you'll turn enterprise AI ambitions into working software. You'll run technical discovery with customer teams, design agentic workflows on Google Cloud Vertex AI, and act as a trusted advisor to both engineers and executives — moving fast from whiteboard concept to a live, production-grade MVP.
Responsibilities
- Design and build agentic systems
- 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
- Prepare data for AI systems
- Design vector databases, RAG pipelines, and chunking strategies that make agents effective in production
- Curate and structure data so agents are ready for both internal and customer-facing use
- Lead discovery and scoping
- Run discovery workshops with customer leadership to define objectives, constraints, and success metrics
- Scope and deliver MVPs in weeks, not months
- Present technical roadmaps that connect AI capabilities to business outcomes
- Advise and enable customers
- Serve as the primary technical point of contact for enterprise accounts, from engineers to C-level stakeholders
- Run workshops and demos, and transfer knowledge so customers can sustain and extend what you've built
- Educate stakeholders honestly on AI capabilities and limitations, building the trust that drives adoption
Requirements
- 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
- 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
Skills
- Gen AI
- Agentic workflows
- Google Cloud Vertex AI
- Agent Development Kit (ADK)
- Model Context Protocol (MCP)
- Agent-to-Agent (A2A) protocols
- Vertex AI Agent Builder
- LangChain
- LlamaIndex
- Python
- TensorFlow
- PyTorch
- scikit-learn
- Hugging Face
- LLM applications
- RAG pipelines
- Vector embeddings
- Prompt engineering
- Google Cloud Platform
- BigQuery
- Cloud Run
- Data preparation
- Feature engineering
- Docker
- Kubernetes
- CI/CD pipelines
Location
- Hybrid remote, Austin, TX 78701
Work Type
- Full-time
- Hybrid remote
Experience Level
- 4+ years designing and deploying AI/ML solutions
Education Level
- Google Cloud Professional Machine Learning Engineer or Data Engineer certification (or willingness to earn one within 6 months)
Benefits
- Health, dental, and vision insurance
- Paid time off
- Ongoing training and certification support
About the Company
- 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
Equal Opportunity
- 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