Applied AI Engineer
Electric Reliability Council of Texas (ERCOT)17 days ago
Taylor, TX, USASenior
Base Salary
$149k - $205k/yr
Responsibilities
- Translate ambiguous business problems into technical roadmaps and identify data, compliance, latency, and cost constraints.
- Design and build production agentic systems using orchestration frameworks such as LangGraph or Microsoft Agent Framework.
- Implement production RAG pipelines with chunking, embeddings, hybrid search, reranking, vector databases, and retrieval-quality evaluation.
- Build secure connectors that provide standardized agent access to enterprise tools and data.
- Deploy AI applications to managed cloud platforms and integrate them with enterprise systems and collaboration tools.
- Build evaluation suites, tracing, monitoring, debugging, and rollback capabilities for reliable production operation.
- Define scalable application architectures, data flows, integration boundaries, and trade-offs involving reliability, latency, throughput, and cost.
- Create reusable components and reference architectures from successful implementations.
- Work with non-technical business owners and maintain knowledge of evolving LLM capabilities and AI development practices.
Requirements
- At least 5 years of job-related AI/ML or software engineering experience beyond degree requirements.
- Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field, or equivalent education and experience.
- Proven experience building and deploying production-grade autonomous agents rather than prototypes.
- Experience with agent orchestration frameworks such as LangGraph, Microsoft Agent Framework, or comparable tools.
- Experience with production RAG, vector search, and vector databases including pgvector, Azure AI Search, or Databricks Vector Search.
- Strong Python skills and hands-on LLM API integration experience.
- Knowledge of scalable, reliable, maintainable system design and API and integration-boundary design.
- Experience building or extending tools and data connectors for LLM applications and deploying applications on managed cloud platforms.
- Knowledge of AI governance, model lifecycle management, and evaluation methodology.
- Ability to scope ambiguous problems, work autonomously, and collaborate directly with non-technical stakeholders.
- Preferred qualifications include multi-application solution and system architecture, security-by-design, Databricks data-platform experience, regulated-industry experience, multi-agent orchestration, and context engineering.
- Preferred cloud or AI/ML certification such as Azure AI Engineer, AWS Machine Learning, or Databricks.
Benefits
- Hybrid schedule in Taylor, Texas, with two days per week onsite.
Tech Stack
AzureDatabricksDockerGitGitHub ActionsHelmKubernetesOpenShiftOracle DatabasePostgreSQLPowerShellPythonSQL
Categories
About Electric Reliability Council of Texas (ERCOT)
The Electric Reliability Council of Texas (ERCOT) is the independent system operator for most of Texas, operating the state’s electric grid and competitive power markets as a membership-based 501(c)(4) nonprofit. It schedules generation, manages transmission, settles the wholesale market, and administers retail switching. ERCOT serves about 27 million customers—roughly 90% of Texas’s load—over a grid with 54,100+ miles of transmission and 1,250+ generation units. Headquarters: Taylor, Texas.