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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Gen AI Solutions Engineer #122 - **Company:** Cloud Prime - **Location:** Austin, TX, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Continuous Integration, Data Cleansing, DevOps, Data Flow Control, Github, Python (Programming Language), Machine Learning, Tensorflow, Web Application Frameworks, Google Cloud, Feature Engineering, Pytorch, Large Language Models, Prompt Engineering, Generative AI, Scikit Learn, Kubernetes, HuggingFace, Google Cloud Functions, Machine Learning Operations, Docker - **Published:** August 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ab60a70d02e50c50 ## About the Role * 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 We especially encourage you to apply if: You're strong on learning agility and problem-solving even if your background doesn't check every box above. We'd rather hire for trajectory and curiosity than a perfect keyword match. ## Description 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., * 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, * Core AI stack: Vertex AI Agent Builder, ADK, Gemini APIs, LangChain, LlamaIndex, MCP, A2A * ML tooling: Python, TensorFlow, PyTorch, Hugging Face Transformers, RAG pipelines, vector databases * GCP services: BigQuery, Dataflow, Cloud Run, GKE, Pub/Sub, Cloud Functions * DevOps: Docker, Kubernetes, GitHub Actions, Vertex AI Pipelines ## Related Videos - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Got AI ideas but no money? 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