AI Developer

The AI Developer is a foundational member of the new AI team, responsible for building and deploying AI-powered applications, agents, and workflows that turn emerging AI capabilities into practical business value. Working across Microsoft Azure (including Azure Databricks and Azure AI Foundry), Snowflake, and Anthropic’s Claude and Claude Agent SDK, the AI Developer will prototype, build, and productionize solutions spanning generative AI, agentic workflows, and applied machine learning. This role is intentionally broad: the ideal candidate brings a range of technical exposure, a strong foundation in software and data engineering, and - above all - genuine enthusiasm for learning new tools and approaches as the field evolves.

Bachelor’s Degree in Computer Science, Data Science, Engineering, Mathematics, or a related field Two (2) to four (4) years of hands-on experience building and shipping software, data, or AI/ML applications in production Strong proficiency in Python, with experience writing tested, maintainable code Working knowledge of SQL and experience with at least one cloud data platform (e.g., Snowflake, Databricks, or similar) Hands-on exposure to building applications with large language models or generative AI APIs (e.g., Claude, OpenAI, Azure OpenAI, or similar) Familiarity with software engineering fundamentals: version control (Git/GitHub), testing, CI/CD, and common design patterns Strong problem-solving and communication skills, with the ability to work effectively across data science, engineering, and business teams A demonstrated pattern of self-directed learning - picking up new tools, frameworks, and platforms quickly and enthusiastically Comfort operating with ambiguity in a fast-evolving field, including a new team, evolving tools, and undefined processes Direct experience with Microsoft Azure services, particularly Azure Databricks and Azure AI Foundry Direct experience with Anthropic Claude, the Claude Agent SDK, or comparable agent development frameworks Experience with vector databases and embedding/chunking strategies for retrieval and semantic search Experience converting unstructured data (documents, PDFs, emails, images, etc.) into structured, queryable formats for AI/ML use Experience with dbt (data build tool) for building, testing, and documenting data transformation pipelines Familiarity with the Model Context Protocol (MCP) or other tool-calling / agent orchestration patterns Experience with infrastructure-as-code, containerization, or cloud deployment practices Exposure to the energy industry or other data- and asset-intensive industries Contributions to open-source AI projects, personal AI projects, or active participation in the broader AI developer community