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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Solutions Architect - **Company:** Clearway Energy, Inc. - **Location:** Scottsdale, AZ, United States - **Experience:** Expert - **Salary:** $137,000.0 - $155,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, ARM Architecture, Computer Vision, Microsoft Azure, Code Review, Continuous Integration, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Distributed Systems, Github, Supervisory Control and Data Acquisition (SCADA), Python (Programming Language), Machine Learning, Performance Tuning, Rapid Prototyping Process, OpenAI, Tensorflow, DataOps, Search Technologies, SQL Databases, Systems Architecture, Microsoft Agent Framework, Data Logging, Pinecone, Application Enhancement Tool, Pytorch, LangChain, ReactJS, Retrieval-Augmented Generation, Large Language Models, Snowflake, Prompt Engineering, Generative AI, Langfuse — LLM Observability and Analytics Platform, Agentic-AI, Gitlab, Build Management, Pgvector, Containerization, Git Flow, LangSmith, Scikit Learn, Information Technology, HuggingFace, CrewAI, Weaviate, Machine Learning Operations, Api Design, Model Context Protocol, Streamlit Framework, Software Version Control, Automation Anywhere, ChromaDB, Microservices - **Published:** October 25, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=0084be8e75e5547b ## About the Role * Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field, or equivalent practical experience * 5+ years of experience in ML or data engineering, including recent production LLM or RAG work * Proven track record of taking AI projects from proof-of-concept to production. * Strong experience with Snowflake Cortex AI or similar systems for enterprise AI workflows * Hands-on experience with AI agentic frameworks (LangChain, LangGraph, Microsoft Agent Framework, CrewAI, or similar) for building autonomous AI workflows. * Hands-on experience with Model Context Protocol (MCP) for connecting agents to tools and data sources. * Proficiency in integrating LLM APIs and SDKs (OpenAI, Anthropic, Azure OpenAI, Hugging Face) into production applications. * Experience designing and implementing RAG systems, including vector databases (Pinecone, Weaviate, Chroma, pgvector, Snowflake vector search, or similar), embedding models, and retrieval optimization. * Experience building interactive applications with Streamlit, Hex, or similar * Advanced GitHub/GitLab workflows including CI/CD, branching strategies, and code review processes. * Expertise in Python, SQL, and modern ML frameworks (PyTorch, TensorFlow, scikit-learn) * Experience with cloud platforms (AWS, Azure, or GCP) and containerization technologies * Strong understanding of data engineering principles, ETL processes, and data governance Essential Skills * Deep knowledge of machine learning algorithms, particularly time series forecasting and predictive analytics. * Strong prompt engineering skills with experience optimizing LLM performance and implementing function calling/tool use patterns. * Understanding of distributed systems, microservices architecture, and API design * Knowledge of data security, privacy, and compliance requirements * Ability to evaluate and benchmark AI/LLM solutions for accuracy, latency, and cost optimization. What Would Be Nice * Experience in renewable energy, utilities, or energy trading sectors * Experience with advanced AI techniques, such as computer vision for asset monitoring, and AI evaluation frameworks * Familiarity with energy management systems (EMS), SCADA, and industrial IoT platforms * Experience with energy sector data (weather, generation, market pricing) * Knowledge of energy markets, grid operations, and regulatory frameworks * Proficiency with Modal for scalable ML model deployment and orchestration * Certifications in Snowflake, cloud platforms, or energy industry standards. No visa sponsorship or any kind of sponsorship for employment authorization is available for this role, now or in the future. ## Description We are seeking an experienced AI Solutions Architect to join our data team and drive the development and deployment of AI-powered solutions that optimize renewable energy asset development and operations. This role bridges the gap between business needs and technical implementation, focusing on rapid prototyping and scaling proofs-of-concept into production-ready systems. You will work closely with Engineering, Operations, Development, and business stakeholders to deliver AI solutions that enhance our renewable power generation capabilities and optimize internal work processes., * Design and architect end-to-end AI solutions using Snowflake Cortex AI and cloud-native technologies. * Develop rapid proofs-of-concept for Retrieval-Augmented Generation (RAG) applications, operational optimization, process optimization, energy forecasting, and predictive maintenance use cases. * Design and implement AI agentic workflows using frameworks such as LangChain, LangGraph, CrewAI, or similar orchestration tools. * Create interactive demos and prototypes using Streamlit or React for stakeholder validation. * Translate business requirements into technical specifications and system architectures. Technical Implementation * Build and deploy scalable AI/ML models for business process optimization, renewable energy applications, including wind/solar forecasting, and asset performance optimization. * Integrate AI capabilities via APIs and SDKs from providers including OpenAI, Anthropic, Microsoft Foundry, and Hugging Face * Implement MLOps pipelines using Dagster, Snowflake, and LLM observability tooling (such as LangSmith or Langfuse) for model training, deployment, and monitoring. * Design and optimize RAG architectures, including vector database implementation, embedding strategies, and retrieval pipelines. * Ensure robust version control and collaboration practices using GitHub/GitLab. Production Scaling & Operations * Lead the transition of successful prototypes to production-grade applications. * Collaborate with DataOps and platform teams to ensure scalable, secure deployments. * Implement monitoring, logging, and alerting for AI systems in production. * Establish best practices for AI model governance and compliance. Stakeholder Engagement * Conduct requirements gathering sessions with operations and executive teams. * Present technical concepts and ROI analyses to both technical and non-technical stakeholders * Provide training and support to end users on AI-powered tools and dashboards.