AI Solutions Architect

What The Role Is

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.

Location: Scottsdale, AZ

What You'll Be Doing

Solution Design & Development

• 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.

What You'll Bring

Required

• 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.

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