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Applied AI Engineer

ERCOT/Electric Reliability Council of Texas Taylor, TX

Taylor, TX

Today

$145k - $200k/yr Full time Agentic Ai Agentic Automation Ai Agent

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Job Description

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

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