AI Engineer, Agent Systems

Salesforce•Palo Alto, CA

1d•Hybrid

About The Position

Salesforce is seeking a Lead AI Engineer, Agent Systems to join the Salesforce AI Research Incubation Team. In this role, you will design and build production-grade AI agent systems that translate cutting-edge research into scalable, reliable platform capabilities. You will work closely with AI researchers, platform engineers, and product teams to develop agent orchestration frameworks, tool integration, and runtime control systems. This role focuses on system-level intelligence, not on inventing new models, but on making AI agents robust, observable, and controllable in real-world environments. This is a lead-level individual contributor role with strong technical ownership and cross-team influence.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related field.
5+ years of experience in backend or distributed systems engineering, with increasing technical ownership.
Strong proficiency in Python, with experience designing complex backend services.
Experience building production systems involving LLMs, AI services, or agent-like workflows.
Deep understanding of RESTful APIs, service-oriented architectures, and system scalability.
Experience with cloud platforms (AWS, GCP) and containerized environments (Docker, Kubernetes).
Strong debugging skills across distributed systems, including logs, traces, and metrics.
Excellent communication skills and the ability to collaborate across research and engineering teams.

Nice To Haves

Hands-on experience with AI agent frameworks, workflow engines, or orchestration systems.
Familiarity with RAG systems, vector databases, or memory architectures.
Experience implementing safety guardrails, policy enforcement, or AI observability.
Exposure to event-driven architectures and message queues (Kafka, RabbitMQ).
Prior experience in a research-adjacent or incubation environment.

Responsibilities

Design and build AI agent systems, including agent lifecycle management, orchestration, and execution control.
Develop scalable pipelines for tool invocation, agent planning, memory, and state management.
Collaborate with AI researchers to operationalize research outputs into reliable agent behaviors.
Define and enforce best practices for agent reliability, safety, observability, and cost control.
Lead the design of APIs and services supporting agent runtime and coordination.
Drive architectural decisions for agent frameworks and integrations across AI services.
Troubleshoot complex system-level issues involving agent behavior, performance, or failure modes.
Mentor engineers and provide technical leadership within the AI engineering organization.