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About the Role

Build and scale production-grade data infrastructure for agentic AI systems that execute complex, multi-step work with autonomy, state, memory, and tool use. Engineer resilient platforms for long-running agent workflows, multi-agent coordination, and adaptive execution in enterprise environments, delivering 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.

Responsibilities

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.
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.
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.
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.

Requirements

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 in DevOps, SRE, data engineering, or infrastructure engineering for production AI or distributed systems.
Experience with LLM serving, retrieval infrastructure, and runtime control for non-deterministic systems.
Experience with 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.
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.

Skills

DevOps
SRE
Data Engineering
Infrastructure Engineering
Production AI
Distributed Systems
LLM Serving
Retrieval Infrastructure
Runtime Control
Graph-based Orchestration
Agent Orchestration
RAG
Knowledge-graph
Python
Orchestration
Data Pipeline Development
Platform Automation
Multi-agent Systems
Human-in-the-loop Controls
Production Evaluation Frameworks
Observability
Reliability
Metrics
Logging
Tracing
Data Quality Monitoring
Kubernetes
Infrastructure as Code
CI/CD
Enterprise Integration Patterns
RBAC
ABAC

Location

US, Arizona, Phoenix
US, California, Folsom
US, Oregon, Hillsboro

Work Type

On-site

Experience Level

5+ years of experience (with Master's)
3+ years of experience (with PhD)

Education Level

Master's degree in software engineering, Computer Engineering, Information Technology, or related field
PhD in Software Engineering, Computer Engineering, Information Technology, or related field

Salary/Compensations

$195,200.00-275,580.00 USD

Benefits

Competitive pay
Stock bonuses
Health benefits
Retirement benefits
Vacation benefits

About the Company

Intel Foundry strives to make every facet of semiconductor manufacturing state-of-the-art while delighting our customers -- from delivering cutting-edge silicon process and packaging technology leadership for the AI era, enabling our customers to design leadership products, global manufacturing scale and supply chain, through the continuous yield improvements to advanced packaging all the way to final test and assembly.
We ensure our foundry customers' products receive our utmost focus in terms of service, technology enablement and capacity commitments.
Employees in the Foundry Technology Manufacturing are part of a worldwide factory network that designs, develops, manufactures, and assembly/test packages the compute devices to improve the lives of every person on Earth.
Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices.
Intel’s official careers website. Find your next job and take on projects that shape tomorrow’s technology.

Equal Opportunity

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.