Agentic AI Engineer

Fervo Energy Co.; Colorado; Hybrid; USD 103,000 - 159,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

Paid time off

Parental leave

Stock options

Bonus incentive

401(k) match

Job summary

Fervo Energy is seeking an Agentic AI Engineer to design and ship agentic workflows that convert unstructured knowledge and operational data into autonomous capabilities for engineers, operators, and decision-makers in the field.

The role owns end-to-end delivery from framing and architecture to prototyping, deployment, and iteration in production, collaborating with Data Engineering, IT Infrastructure, and domain SMEs to establish reusable patterns for retrieval, grounding, and agent

Qualifications

• 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.
• Strong Python skills, including async patterns, API design with FastAPI, and writing testable production code.
• Demonstrated experience with LangChain/LangGraph, LlamaIndex, AutoGen, or Semantic Kernel.
• Working knowledge of LLM APIs and SDKs (OpenAI, Azure OpenAI), including tool use, structured outputs, and prompt engineering.
• Experience implementing RAG architectures with vector databases and embedding models.
• Experience with agent orchestration patterns including multi-step planning and tool routing.

Responsibilities

• 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) over technical documents, historian data, and operational records.
• Translate domain problems from drilling, completions, production, geophysics, and plant operations into well-scoped agentic use cases with clear success metrics.
• 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.
• Deploy agents as production services on Azure-native stack with Entra ID SSO, Key Vault, and cost controls.
• Lead design reviews and cross-functional enablement sessions on agentic AI patterns and best practices.

Skills

Python

Async patterns

FastAPI

LangChain/LangGraph

LLM tooling

Version control

CI/CD

Docker

Terraform

Observability

Production ops

Education

Bachelor’s or Master’s in Computer Science or related field

Tools

Azure OpenAI

OpenAI

LangChain

LangGraph

LlamaIndex

AutoGen

Semantic Kernel

Databricks

Snowflake

dbt Semantic Layer

Neo4j

Chroma

PGVector

Job description

TheAgentic 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.

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. TheAgentic 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.

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 Qualifications

• 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 Qualifications

• 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)

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 theGoldenorHoustonoffice will be required.

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.

Fervo Energy is an Equal Opportunity Employer and does not discriminate on the basis of race, color, creed, gender, religion, marital status, registered domestic partner status, age, national origin, ancestry, physical or mental disability, medical condition, sex, genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws. It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics or is associated with a person who has or is perceived as having any of those characteristics.

Fervo Energy commercializes technology to own, develop, and operate geothermal assets as the dispatchable foundation to a 100% clean energy future. As a 24/7, reliable clean energy resource, geothermal energy has a major role to play in the future electric grid and Fervo Energy’s key innovations bring a full suite of modern technology to make geothermal cost competitive. Fervo’s innovations include technologies such as advanced computational models, horizontal drilling, and distributed fiber optic sensing that we have developed with partners including Schlumberger, ARPA‑E, and the Lawrence Berkeley National Lab. Fervo is supported through Cyclotron Road, the Department of Energy, Stanford University, and Breakthrough Energy Ventures.

Fervo Energy is an Equal Opportunity Employer and does not discriminate on the basis of race, color, creed, gender, religion, marital status, registered domestic partner status, age, national origin, ancestry, physical or mental disability, medical condition, sex, genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws. It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics or is associated with a person who has or is perceived as having any of those characteristics.