Experience building RAG and knowledge-graph-backed systems for LLM applications in production. * Python skills for orchestration, data pipeline development, and platform automation Preferred ...
Experience building RAG and knowledge-graph-backed systems for LLM applications in production. * Python skills for orchestration, data pipeline development, and platform automation Preferred ...
OR · On-site
Working on graph data management and knowledge discovery over one of the world's largest grocery catalogs, and integrating structured knowledge with LLM-based reasoning and natural language ...
Data Scientist I or II (MAD-BS-OR)
Hillsboro, OR · On-site +1
$121K - $167K/yr
Implement LLM-based systems with: * Tool-calling frameworks * Retrieval-Augmented Generation (RAG ... Knowledge of: * Signal processing or physics-based modeling * Graph-based reasoning or causal ...
Data Scientist I or II (MAD-BS-OR)
Hillsboro, OR · On-site +1
$121K - $167K/yr
Implement LLM-based systems with: * Tool-calling frameworks * Retrieval-Augmented Generation (RAG ... Knowledge of: * Signal processing or physics-based modeling * Graph-based reasoning or causal ...
Data Scientist I or II (MAD-BS-OR)
Hillsboro, OR · On-site
$121K - $167K/yr
Implement LLM-based systems with: * Tool-calling frameworks * Retrieval-Augmented Generation (RAG ... Knowledge of: * Signal processing or physics-based modeling * Graph-based reasoning or causal ...
Data Scientist I or II (MAD-BS-OR)
Hillsboro, OR · On-site
$121K - $167K/yr
Implement LLM-based systems with: * Tool-calling frameworks * Retrieval-Augmented Generation (RAG ... Knowledge of: * Signal processing or physics-based modeling * Graph-based reasoning or causal ...
Senior AI Engineer - Agentic Systems
Portland, OR · On-site
$110K - $152K/yr
Required Experience 6+ years of production software engineering, with at least 2 years building LLM ... Experience with knowledge graphs or graph-augmented retrieval. Familiarity with construction, AEC ...
Senior AI Engineer - Agentic Systems
Portland, OR · On-site
$110K - $152K/yr
Required Experience 6+ years of production software engineering, with at least 2 years building LLM ... Experience with knowledge graphs or graph-augmented retrieval. Familiarity with construction, AEC ...
... graph databases (Neo4j). * Knowledge of Kubernetes, GitOps, or advanced cloud-native patterns ... and deploying GenAI/LLM-powered solutions in client or production environments * 1+ years of ...
... graph databases (Neo4j). * Knowledge of Kubernetes, GitOps, or advanced cloud-native patterns ... and deploying GenAI/LLM-powered solutions in client or production environments * 1+ years of ...
... graph databases (Neo4j). * Knowledge of Kubernetes, GitOps, or advanced cloud-native patterns ... and deploying GenAI/LLM-powered solutions in client or production environments * 1+ years of ...
... graph databases (Neo4j). * Knowledge of Kubernetes, GitOps, or advanced cloud-native patterns ... and deploying GenAI/LLM-powered solutions in client or production environments * 1+ years of ...
Knowledge of web security, GitOps, and Kubernetes customization. * Basic understanding of AI/ML ... Hands-on experience building and deploying GenAI/LLM-powered solutions in client or production ...
Knowledge of web security, GitOps, and Kubernetes customization. * Basic understanding of AI/ML ... Hands-on experience building and deploying GenAI/LLM-powered solutions in client or production ...
Knowledge of web security, GitOps, and Kubernetes customization. * Basic understanding of AI/ML ... Hands-on experience building and deploying GenAI/LLM-powered solutions in client or production ...
Knowledge of web security, GitOps, and Kubernetes customization. * Basic understanding of AI/ML ... Hands-on experience building and deploying GenAI/LLM-powered solutions in client or production ...
Llm Knowledge Graph information
What is the difference between Llm Knowledge Graph vs Data Scientist?
| Aspect | Llm Knowledge Graph | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of NLP, graph databases, machine learning | Statistics, programming, data analysis |
| Work Environment | Research labs, AI companies, tech firms | Corporate, consulting, research institutions |
| Industry Usage | AI, knowledge management, semantic web | Business analytics, predictive modeling |
While both roles involve data and machine learning, Llm Knowledge Graph specialists focus on building interconnected knowledge bases using NLP and graph technologies, whereas Data Scientists analyze data to extract insights and build predictive models. The roles often overlap in AI projects but serve different core functions within organizations.
Full-time
Medical, Retirement, PTO
Re-posted 12 days ago
Intel rating
8.7
Based on 146 frontline employees who took The Breakroom Quiz
11th of 142 rated electronics manufacturers
Job description
Build and scale production-grade data infrastructure for agentic AI systems that execute complex, multi-step work with autonomy, state, memory, and tool use. You will engineer resilient platforms for long-running agent workflows, multi-agent coordination, and adaptive execution in enterprise environments - while delivering the data pipelines and integrations that connect agents to enterprise data, legacy systems, and simulation tools. This role emphasizes reliability, control, observability, data quality, and governance for agentic AI systems over conversational chatbot patterns.
Key Responsibilities
Agentic Orchestration
Productionize graph-based orchestration for planner-executor-validator, orchestrator-worker, and similar patterns.
Implement explicit state and control flows: branching, loops, routing, interruption points, and human approval checkpoints.
Enable robust agent-tool integration across APIs, services, data systems, and enterprise platforms.
Support multi-agent collaboration patterns with guardrails for coordination, delegation, and convergence.
Data Engineering and Integration
Design and maintain data pipelines connecting distributed enterprise data to a centralized semantic/knowledge layer that ensures clean, unified inputs for agent consumption.
Build and operate event streaming, API management, and systems integration infrastructure to enable trustable, consistent data for agentic workflows.
Build and maintain a data catalog and onboarding guides for teams adopting the agentic platform.
DevOps and Reliability for AI Agent Systems
Define and track SLIs/SLOs for task completion reliability, reasoning quality, tool-call success, latency, and cost across agent pipelines.
Implement CI/CD practices tailored for agent deployments - versioning agent configurations, prompts, tools, and orchestration logic as code.
Build incident response and reliability practices for autonomous workflows, including safe rollback, pause/resume, and controlled retries.
Optimize compute, storage, and inference paths for sustained agent throughput and cost efficiency.
Observability, Evaluation, and Control
Implement full-stack observability for agent runs - traces, state transitions, tool telemetry, data quality signals, outcomes, and replay ability.
Build continuous evaluation pipelines for agent behavior, including correctness, safety, drift, and regression detection.
Provide actionable operational dashboards for quality, reliability, data health, and cost in production agent systems.
Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.
Minimum Qualifications
- Master's degree in software engineering, Computer Engineering, Information Technology, or related field with 5+ years of experience.
- OR PhD in Software Engineering, Computer Engineering, Information Technology, or related field with 3+ years of experience.
Experience listed above should be in at least one of the following:
- DevOps, SRE, data engineering, or infrastructure engineering for production AI or distributed systems.
- LLM serving, retrieval infrastructure, and runtime control for non-deterministic systems.
- Graph-based or agent orchestration frameworks.
- Experience building RAG and knowledge-graph-backed systems for LLM applications in production.
- Python skills for orchestration, data pipeline development, and platform automation
Preferred Qualifications
Experience designing multi-agent systems with clear autonomy boundaries and human-in-the-loop controls.
Track record in production evaluation frameworks for agent quality and safety.
Experience with observability and reliability for data and agent pipelines (metrics, logging, tracing, data quality monitoring).
Strong experience with Kubernetes, infrastructure as code, CI/CD, and production observability.
Deep experience with enterprise integration patterns and tools (e.g., RBAC, ABAC).
Ability to package data engineering practices into developer-friendly tooling and documentation.
Experience in regulated or enterprise environments requiring high trust and auditability.
Join Intel and be part of a mission to lead the AI revolution. Innovate with us and shape the future of technology today.
We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel.
Annual Salary Range for jobs which could be performed in the US: $195,200.00-275,580.00 USDThe range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.Work Model for this Role
This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.*
ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.About Intel
Sourced by ZipRecruiter
Intel strives to make every facet of semiconductor manufacturing state-of-the-art -- from semiconductor process development and manufacturing, through yield improvement to packaging, final test and optimization, and world class Supply Chain and facilities support. Employees in the Technology and Manufacturing Group are part of a worldwide network of design, development, manufacturing, and assembly/test facilities, all focused on utilizing the power of Moore's Law to bring smart, connected devices to every person on Earth
Industry
Manufacturing
Company size
10,000+ Employees
Headquarters location
Santa Clara, CA, US
Year founded
1968