Interested in this AI/ML Engineer role at ERCOT/Electric Reliability Council of Texas?
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About This Role
At ERCOT, our diverse and dynamic work environment provides a platform on which employees can work together to build the future of the Texas power grid and wholesale market utilizing the latest technologies and resources. We encourage you to join our talented, dedicated workforce to develop world-class solutions for today and tomorrow’s energy challenges while learning new skills and growing your career.
ERCOT is committed to fostering inclusion at all levels of our company. It is the cornerstone of our corporate values of accountability, leadership, innovation, trust, and expertise. We know that individuals with a wide variety of talents, ideas, and experiences propel the innovation that drives our success. An inclusive and diverse workforce strengthens us and allows for a collaborative environment to solve the challenges that face our industry today and in the future.
JOB SUMMARY
Applies knowledge of generative AI application development, agent orchestration, retrieval architecture, and evaluation methodology to deliver production AI solutions. Follows established AI governance, security review, and evaluation processes to deploy reliable, auditable systems. Operates in a regulated environment where delivery speed is balanced against required governance and review gates.
JOB DUTIES:
- 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.
Required skills and knowledge:
- 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.
Preferred:
- 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
TOOLS & TECHNOLOGY
- 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 (Private Cloud / On-Premises), Docker, Kubernetes, Helm
- CI/CD & Source Control: GitHub, GitHub Actions, Git and pull-request workflows
- Observability & Evaluation: Tracing, evaluation harnesses, LLM observability, logging and monitoring
- ITSM & Agile Tooling: ServiceNow, Jira
- Scripting & Languages: Python, PowerShell
CERTIFICATION
- Cloud or AI/ML certification (Azure AI Engineer, AWS Machine Learning, or Databricks) (Preferred)
WORK LOCATION:
- Hybrid schedule in Taylor, TX 2 days per week.
- #LI-DN
ERCOT is firmly committed to equal employment for all qualified persons without regard to race, sex, medical condition, religion, age, creed, national origin, citizenship status, marital status, sexual orientation, physical or mental disability, ancestry, veteran status, genetic information or any other protected category under federal, state or local law.
Expected Salary Range:
$145,000 - $200,000
Salary Context
This $145K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ERCOT/Electric Reliability Council of Texas, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($172K) sits 19% below the category median. Disclosed range: $145K to $200K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
ERCOT/Electric Reliability Council of Texas AI Hiring
ERCOT/Electric Reliability Council of Texas has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Taylor, TX, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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