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Listing evidence and provenance
- Source
- Workday View original listing
- Freshness
- Open in the current public read path; this is not an employer guarantee.Last observed October 31, 2026
- Classification
- Modern power-system classification not yet published for this listing.Legacy source industry field: Grid Infrastructure
- Pay provenance
- Employer-provided pay
Job Description
CERTIFICATION
Key Responsibilities
- Translates ambiguous business problems into scoped technical roadmaps, and identifies constraints (data access, compliance, latency, cost) before development begins.
- Designs and builds production agentic systems, covering planning, tool-calling, multi-step reasoning, memory, and error recovery, using modern orchestration frameworks such as LangGraph or Microsoft Agent Framework.
- Implements production RAG pipelines, including chunking, embeddings, hybrid search, reranking, retrieval-quality evaluation, and content freshness.
- Builds and extends connectors that give agents secure, standardized access to enterprise tools and data.
- Deploys applications onto managed cloud platforms and integrates them with enterprise systems and collaboration tools.
- Builds evaluation suites, tracing, and rollback paths so agents are reliable in production rather than demonstrations.
- Monitors, debugs, and improves deployed applications against evaluation metrics.
- Applies sound system-design principles. Defines application architecture, data flows, and integration boundaries, and designs for scalability, reliability, latency, and cost.
- Codifies repeatable patterns, turning successful builds into reusable components and reference architecture the team can leverage.
- Works directly with non-technical business owners to understand their workflows, and maintains current knowledge of evolving LLM capabilities, implementation patterns, and AI development stacks.
- EXPERIENCE
- Requires minimum 5 years job related work experience in AI/ML or software engineering in excess of degree requirements.
Requirements
- Proven record of building and deploying production-grade autonomous agents, not prototypes.
- Agent orchestration frameworks (LangGraph, Microsoft Agent Framework, or comparable).
- Production RAG with vector search and vector databases (pgvector, Azure AI Search, Databricks Vector Search)
- Strong Python and hands-on LLM API integration.
- System-design fundamentals: scalable, reliable, maintainable services, API and integration-boundary design, and trade-offs across latency, throughput, and cost.
- Building or extending tool and data connectors for LLM applications.
- Deploying and operating applications on a managed cloud platform.
- AI governance, model lifecycle, and evaluation methodology.
- Stakeholder and discovery skills. Works directly with non-technical business owners, scopes ambiguity, and operates autonomously.
- Solution and system architecture across multiple applications, with security-by-design and reference architecture.
- Large-scale data platforms (Databricks) for retrieval, feature, or pipeline work.
- Experience in a regulated industry (energy, finance, healthcare) or an audit-driven environment.
- Multi-agent orchestration and context engineering.
- EDUCATION
- Bachelor's Degree: Computer Science, Data Science, Information Systems, Engineering, or related field (Required) or a combination of education and experience that provides equivalent knowledge to a major in such fields is required
Ready to Apply?
Apply directly on ERCOT's site
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