Gen AI Solutions Engineer #122

About the role

Premier Cloud is seeking a Gen AI Solutions Engineer to turn enterprise AI ambitions into working software. As a Gen AI Solutions Engineer, 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 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 by designing vector databases, RAG pipelines, and chunking strategies
• Curate and structure data so agents are ready for both internal and customer-facing use
• Lead discovery and scoping by running discovery workshops with customer leadership to define objectives, constraints, and success metrics
• Scope and deliver MVPs in weeks, not months, and present technical roadmaps that connect AI capabilities to business outcomes
• Advise and enable customers by serving as the primary technical point of contact for enterprise accounts, running workshops and demos, and transferring knowledge
• 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

Preferred Qualifications

• 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

Technical Environment

The core AI stack includes Vertex AI Agent Builder, ADK, Gemini APIs, LangChain, LlamaIndex, MCP, and A2A. The ML tooling includes Python, TensorFlow, PyTorch, Hugging Face Transformers, RAG pipelines, and vector databases. The GCP services used include BigQuery, Dataflow, Cloud Run, GKE, Pub/Sub, and Cloud Functions. DevOps tools include Docker, Kubernetes, GitHub Actions, and Vertex AI Pipelines.

Benefits

• Health, dental, and vision insurance
• Paid time off
• Ongoing training and certification support