Senior AI Systems Engineer

Systems Planning and Analysis•El Segundo, CA

16h•Hybrid

About The Position

SPA is seeking a Senior AI Systems Engineer to design, build, and operationalize AI capabilities across secure, mission-critical systems and platforms. This role is execution-heavy and hands-on, focused on transforming frontier AI capabilities into reliable, auditable, production-grade solutions that scale across teams and environments. Please note this is not an AI research role. You will not be training foundation models or pushing theoretical boundaries. Instead, you will act as a senior engineer who understands how to apply modern AI technologies responsibly and effectively by building agents, services, and platform capabilities that measurably improve system performance, operational awareness, automation, and developer productivity. You will work under the direction of senior engineering leadership (Chief Engineer, Chief Architect, Integration Lead) and collaborate closely with platform engineers, software teams, security stakeholders, and operators. Your success will be measured by working software in production, adoption by teams, and the establishment of repeatable, secure AI patterns. This is a remote/hybrid position. You will be expected to work on-site at least 2 days per week in either Colorado Springs, CO or El Segundo, CA due to the fast pace, quick-turn nature of the job and the persistent need to access secure networks. Thus, residing in comfortable driving distance of either Colorado Springs, CO or El Segundo, CA should be considered.

Requirements

US Citizen and current active DoD Secret clearance
Minimum 8 years of professional software engineering experience, with a strong foundation in backend or platform engineering
Bachelor’s degree in Computer Science, Engineering, or related field
Demonstrated experience shipping and maintaining production systems
Strong proficiency in at least one modern programming language (e.g., Python, Go, Java, TypeScript)
Hands-on experience integrating foundation models via APIs (e.g., OpenAI-compatible or equivalent services)
Experience building AI agents, orchestration workflows, or tool-using models
Familiarity with retrieval-augmented generation (RAG), embeddings, vector search, and knowledge-based AI patterns
Practical understanding of AI failure modes, hallucinations, and mitigation strategies
Experience designing or contributing to platform services, shared tooling, or developer enablement capabilities
Familiarity with cloud-native architectures, containerization, and service-based systems
Experience working with logs, metrics, telemetry, or operational analytics
Experience implementing systems in secure, regulated, or high-consequence environments
Understanding of access control, audit logging, and secure API design
Familiarity with Responsible AI concepts such as human-in-the-loop, explainability, and governance (practical application, not policy theory)

Responsibilities

Design and implement production AI-enabled services using modern foundation models via secure APIs.
Build AI agents and agent-orchestrated workflows that support decision-making, automation, and system intelligence.
Develop reference implementations (code, patterns, architectures) that other teams can adopt and extend.
Integrate AI with structured and unstructured data sources, including knowledge graphs, embeddings, retrieval pipelines, and system telemetry.
Implement human-in-the-loop mechanisms for oversight, escalation, and trust in high-consequence workflows.
Expand AI usage beyond chat interfaces into platform capabilities, such as: log and telemetry analysis operational anomaly detection self-healing and automated remediation runbook generation and improvement documentation synthesis and maintenance
Enable downstream development teams to safely consume AI capabilities within their own applications through shared services, APIs, and patterns.
Establish reusable approaches for AI integration across environments, rather than one-off implementations.
Design AI solutions that operate in secure and controlled environments, with strong attention to: access control and least privilege auditability and traceability data handling boundaries logging and monitoring
Embed Responsible AI principles into system design, including transparency, controllability, and failure awareness.
Partner with security and platform teams to ensure AI capabilities meet operational, compliance, and accreditation expectations.
Write production code weekly, not just architecture diagrams.
Prototype, harden, and productionize AI-enabled capabilities.
Participate in code reviews, design reviews, and technical decision-making.
Balance speed with rigor—moving fast without breaking trust.