Applied AI Engineer

ERCOT/Electric Reliability Council of Texas is seeking an Applied AI Engineer to build and deploy production generative AI solutions in a regulated setting. The work includes agentic systems and retrieval augmented generation (RAG) pipelines, with a focus on governance, evaluation, and dependable operations once models move beyond demonstrations.

This hybrid role is based in Taylor, TX (2 days per week) and supports the design, integration, monitoring, and continuous improvement of AI applications across enterprise tools and data systems.

What you will do

• Convert ambiguous business needs into scoped technical roadmaps, identifying constraints such as data access, compliance, latency, and cost before development starts.
• Design and deliver production agentic systems, including planning, tool-calling, multi-step reasoning, memory, and error recovery using orchestration frameworks such as LangGraph or Microsoft Agent Framework.
• Implement production RAG pipelines with chunking, embeddings, hybrid search, reranking, retrieval-quality evaluation, and content freshness mechanisms.
• Build and extend connectors that provide agents secure, standardized access to enterprise tools and data.
• Deploy AI applications to managed cloud platforms and integrate them with enterprise systems and collaboration tools.
• Create evaluation suites, tracing, and rollback paths to support reliable production behavior rather than one-off demos.
• Monitor, debug, and improve deployed applications against evaluation metrics.
• Apply system design principles by defining architectures, data flows, and integration boundaries with attention to scalability, reliability, latency, and cost.
• Codify repeatable patterns by converting successful builds into reusable components and reference architecture for the team.
• Partner with non-technical business owners to understand workflows, maintain current knowledge of evolving LLM capabilities and implementation patterns, and operate with autonomy through ambiguity.

Key qualifications

• Proven experience building and deploying production-grade autonomous agents, not prototypes.
• Experience with agent orchestration frameworks, including 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 of LLM APIs.
• System design fundamentals for scalable, reliable, maintainable services, including API design and trade-offs across latency, throughput, and cost.
• Experience building or extending tool and data connectors for LLM applications.
• Ability to deploy and operate applications on a managed cloud platform.
• Understanding of AI governance, model lifecycle, and evaluation methodology.
• Strong stakeholder and discovery skills, including scoping ambiguity and working directly with non-technical business owners.

Technologies you may work with

• Agent & LLM frameworks: LangGraph, Microsoft Agent Framework, LangChain, LlamaIndex
• LLM platforms & APIs: Claude API, Azure OpenAI, OpenAI API, plus 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 & integrations: MCP (Model Context Protocol), REST APIs, enterprise system connectors, Teams integration
• Cloud & deployment: Azure, OpenShift (private cloud/on-premises), Docker, Kubernetes, Helm
• CI/CD & source control: GitHub, GitHub Actions
• Observability & evaluation: Tracing, evaluation harnesses, LLM observability, logging and monitoring
• ITSM & agile tooling: ServiceNow, Jira
• Scripting & languages: Python, PowerShell

Preferred experience

• Solution and system architecture across multiple applications with security-by-design and reference architecture.
• Large-scale data platform experience with Databricks for retrieval, feature, or pipeline work.
• Experience in a regulated or audit-driven environment (energy, finance, healthcare).
• Multi-agent orchestration and context engineering.

Education and salary

• Minimum experience: 5 years
• Education: Bachelor’s Degree in Computer Science, Data Science, Information Systems, Engineering, or related field (or a combination of education and experience providing equivalent knowledge to such a major)
• Salary: USD 145,000 - 200,000 per year

Certification (preferred)

• Cloud or AI/ML certification such as Azure AI Engineer, AWS Machine Learning, or Databricks