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About This Role
Description:
Fervo is building the most cost\-effective, repeatable geothermal power plants in the world. Scaling that mission requires AI\-native capabilities that drive measurable impact across drilling, completions, production, geophysics, and power plant operations. The Agentic AI Engineer, within the Data \& AI team, designs and ships agentic workflows that turn unstructured knowledge and structured operational data into autonomous capabilities for engineers, operators, and decision\-makers in the field.
The Agentic AI Engineer owns the end\-to\-end delivery of agentic AI use cases — from problem framing and architecture, through prototyping, evaluation, deployment, and iteration in production. Working across Data Engineering, IT Infrastructure, domain SMEs, and business stakeholders, this role establishes reusable patterns for retrieval, semantic grounding, tool integration, and agent orchestration on top of our Azure, Databricks, and Snowflake stack. Success requires strong hands\-on engineering depth, sound architectural judgment, and pragmatism about what to ship versus what to defer.
Requirements:
*Responsibilities*
Agentic Workflow Design \& Delivery
- Design and deploy end\-to\-end agentic AI workflows using planner–worker, orchestrator–executor, multi\-agent, and RAG\-based architectures
- Build reusable components and reference patterns for tool routing, state management, error handling, and human\-in\-the\-loop checkpoints
- Implement robust retrieval pipelines (hybrid search, vector \+ keyword, graph\-aware retrieval) over technical documents, historian data, and operational records
- Translate domain problems from drilling, completions, production, geophysics, and power plant operations into well\-scoped agentic use cases with clear success metrics
Semantic Grounding \& Knowledge Integration
- Build and maintain a semantic layer over our data lake and warehouse using Snowflake Semantic Views and Databricks Unity Catalog Metric Views, making business concepts queryable by both humans and agents
- Develop and curate knowledge graphs that connect domain entities (wells, pads, assets, equipment, events, documents) and serve as grounding context for LLM reasoning
- Standardize how agents access enterprise data through Model Context Protocol (MCP) servers and equivalent integration patterns
Evaluation, Observability \& Production Operations
- Establish agent evaluation frameworks including golden datasets, automated regression tests, and structured evals for accuracy, faithfulness, and tool\-use correctness
- Implement tracing, logging, and observability across agent runs to support debugging, cost monitoring, and continuous improvement
- Build feedback loops that capture user input and convert it into eval cases and prompt/system improvements
- Support production incidents and platform\-level issues impacting deployed agents
Deployment \& Enablement
- Deploy agents as production services on our Azure\-native stack (App Service, Container Apps, Functions) with Entra ID SSO, Key Vault\-managed secrets, and proper cost controls
- Build lightweight UIs (Streamlit, Gradio, or React) for agentic applications and internal tools
- Lead design reviews and cross\-functional enablement sessions on agentic AI patterns and best practices
*Qualifications*
Required
- Bachelor's or Master's degree in Computer Engineering or Data Science preferred.
- 2\+ years of hands\-on experience building and deploying agentic AI or LLM\-powered applications in production, not just prototypes or notebooks
- Strong Python skills, including async patterns, API design with FastAPI, and writing testable, maintainable production code
- Demonstrated experience with at least one major agent framework: LangChain/LangGraph, LlamaIndex, AutoGen, or Semantic Kernel
- Working knowledge of LLM APIs and SDKs (Anthropic Claude, OpenAI, Azure OpenAI), including tool use/function calling, structured outputs, streaming, and prompt engineering
- Experience implementing RAG architectures with vector databases (Azure AI Search, pgvector, Pinecone, Weaviate, Chroma, or similar) and embedding models
- Experience with agent orchestration patterns including multi\-step planning, tool routing, state management, and graceful failure handling
- Familiarity with Model Context Protocol (MCP) or equivalent standards for tool and context integration
- Cloud deployment experience on Azure (App Service, Container Apps, Functions, Key Vault, Entra ID), or equivalent in AWS/GCP with willingness to work in our Azure\-first environment
- Strong Git and CI/CD experience, including version control discipline, code review, and automated testing
- Experience with containerization (Docker) and infrastructure\-as\-code (Terraform preferred)
- Strong observability and production operations skills, including structured logging, tracing, cost monitoring, and runbook development
Preferred
- Experience designing or working with semantic models and semantic layers — Snowflake Semantic Views, Databricks Metric Views, dbt Semantic Layer, Cube, or Power BI semantic models
- Hands\-on experience with knowledge graphs: graph databases (Neo4j, Azure Cosmos DB Gremlin), RDF/SPARQL, ontology design, or graph\-augmented RAG
- Experience with agent evaluation tooling such as LangSmith
- Experience with our broader data stack: Databricks, Snowflake, Azure Data Lake Storage (ADLS), Azure Data Factory
- Oil and gas or energy industry experience, including familiarity with drilling, completions, production, geophysics, or industrial historian data
- Background in time\-series data, signal processing, or industrial IoT (MQTT, OPC UA, SparkplugB)
Experience with multimodal models for handling well logs, schematics, or scanned reports
*Location*
Fervo Energy is headquartered in Houston, TX, with growing offices in Golden, CO, Reno, NV, and Oakland, CA, and Salt Lake City, UT. This position will be eligible for some hybrid work flexibility, but regular in\-office presence at the Golden or Houston office will be required*.*
*Compensation \& Benefits*
Fervo provides a comprehensive suite of benefits including medical, dental, vision, life, short\-term and long\-term disability, flexible paid time off, and paid parental leave. Additionally, Fervo offers an incentive stock options program, a bonus incentive program, and a 401(k) plan with an employer match.
Fervo Energy is providing the compensation range and general description of other compensation and benefits that the company in good faith believes it might pay and/or offer for this position based on the successful applicant’s education, experience, knowledge, skills, and abilities in addition to internal equity and geographic location. Expected Salary: $103,152 \- $158,588 based on location and experience.
Fervo Energy reserves the right to ultimately pay more or less than the posted range and offer other compensation, depending on circumstances not related to an applicant’s sex or other status protected by local, state, or federal law.
Salary Context
This $103K-$158K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Fervo Energy, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($130K) sits 27% below the category median. Disclosed range: $103K to $158K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Fervo Energy AI Hiring
Fervo Energy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $158K - $158K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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