Applied AI Engineer

ERCOT, the Electric Reliability Council of Texas, is seeking an Applied AI Engineer to build and deploy production generative AI capabilities in a regulated environment. The role focuses on architecting reliable systems, including agentic workflows and RAG pipelines, with governance, evaluation, and dependable operations.

What you’ll do

• Translate ambiguous business problems into scoped technical roadmaps, identifying constraints such as data access, compliance, latency, and cost before development starts.
• Design and build production agentic systems covering planning, tool-calling, multi-step reasoning, memory, and error recovery using orchestration frameworks such as LangGraph or Microsoft Agent Framework .
• Implement production RAG pipelines, including chunking, embeddings, hybrid search, reranking, retrieval-quality evaluation, and content freshness.
• Build and extend connectors that provide agents secure, standardized access to enterprise tools and data.
• Deploy applications to managed cloud platforms and integrate them with enterprise systems and collaboration tools.
• Create evaluation suites, tracing, and rollback paths so agent behavior is reliable in production, not limited to demonstrations.
• Monitor, debug, and continuously improve deployed applications using evaluation metrics.
• Apply system design fundamentals by defining architecture, data flows, and integration boundaries with attention to scalability, reliability, latency, and cost.
• Codify repeatable patterns by turning successful builds into reusable components and reference architecture.
• Work with non-technical business owners to understand workflows, maintain awareness of evolving LLM capabilities, and apply current implementation patterns and AI development stacks.

Requirements

• Proven experience building and deploying production-grade autonomous agents, not prototypes.
• Experience with agent orchestration frameworks such as LangGraph , Microsoft Agent Framework , or comparable tools.
• Production RAG experience using vector search and vector databases such as pgvector , Azure AI Search , or Databricks Vector Search .
• Strong Python skills and hands-on integration with LLM APIs.
• System design fundamentals including scalable, reliable, maintainable services, API and integration-boundary design, and trade-offs across latency, throughput, and cost.
• Ability to build or extend tool and data connectors for LLM applications.
• Experience deploying and operating applications on a managed cloud platform.
• Knowledge of AI governance, model lifecycle practices, and evaluation methodology.
• Stakeholder and discovery skills to scope ambiguity, work with non-technical business owners, and operate autonomously.

Technologies you’ll work with

• Agent & LLM frameworks: LangGraph, Microsoft Agent Framework, LangChain, LlamaIndex
• LLM platforms & APIs: Claude API, Azure OpenAI, OpenAI API, model routing and evaluation frameworks
• AI coding assistants: Claude Code, OpenAI Codex, GitHub Copilot, Microsoft Copilot Studio
• Retrieval & vector search: Azure AI Search, Databricks Vector Search, pgvector
• Data & analytics: Databricks, Power BI, SQL, Oracle DB, PostgreSQL
• Connectors & integration: MCP (Model Context Protocol), REST APIs, enterprise system connectors, Teams integration
• Cloud & deployment: Azure, OpenShift, Docker, Kubernetes, Helm
• CI/CD & source control: GitHub, GitHub Actions, Git pull-request workflows
• Observability & evaluation: Tracing, evaluation harnesses, LLM observability, logging and monitoring
• ITSM & Agile tools: ServiceNow, Jira
• Scripting: Python, PowerShell

Preferred qualifications

• Solution and system architecture across multiple applications, including security-by-design and reference architecture.
• Experience with large-scale data platforms such as Databricks for retrieval, feature work, or pipeline development.
• Experience in regulated industries (energy, finance, healthcare) or audit-driven environments.
• Background in multi-agent orchestration and context engineering.

Education, location, and compensation

• Minimum experience: 5 years
• Education: Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field (or equivalent knowledge gained through a combination of education and experience)
• Certification (preferred): Cloud or AI/ML certification such as Azure AI Engineer, AWS Machine Learning, or Databricks
• Location: Taylor, TX (hybrid, 2 days per week)
• Salary: USD 145,000 - 200,000 per year