​The Leidos Cyber Accelerator is seeking a hands-on applied researcher to invent, prototype, and operationalize agentic AI systems, and the automation required to deploy them in enterprise environments. You will evaluate emerging agentic patterns (multi-agent coordination, tool use, human-in-the-loop oversight), implement them as working systems, and build reusable automation and integration patterns (e.g., LLM-driven tool connectors/protocols) that enable secure, observable, repeatable paths for deployment, validation, and security testing. 

​Primary Responsibilities: 
-Comfortable with modern tools to support configuration and software development (examples: Codex, Claude Code). 
-Research, prototype, and evaluate multi-agent architectures (supervisor/worker, hierarchical, HITL) and translate successful patterns into reusable libraries/services. 
-Agent/orchestration frameworks (examples: LangGraph-style state machines, AutoGen-style agent teams, CrewAI-style workflows) 
-Build secure agent-to-tool integrations using open connectivity standards. 
-Agent tool-integration protocols (examples: Model Context Protocol (MCP), structured tool/function calling patterns) 
-Create deployment automation for agentic services: reproducible environments and repeatable enterprise releases on Kubernetes. 
-IaC & environment provisioning  (e.g., Terraform) 
-Kubernetes packaging & configuration management (examples: Helm, Kustomize) 
-Stand up production-like stacks on AWS + Kubernetes and codify them into repeatable, automated, tooling. 
-Debug and refine AI-driven workflows, ensuring that work can be generalized. 
-Create trials and collect metrics for demonstrating success of automated deployments and connectivity between enterprise components. 
-Build observability-first agent systems (tool-call telemetry, step tracing, eval hooks). 
-Research and apply nonhuman/workload identity patterns for agentic services and integrate into deployment automation. 
-Model serving/inference platforms (examples: Ollama, vLLM, Ray Serve) 
-Observability/instrumentation standards (examples: OpenTelemetry) and common backends (examples: Prometheus, Grafana, Jaeger) 
(Optional, as mission needs) Experiment with scalable serving patterns for LLM endpoints on Kubernetes. 

​Basic Qualifications: 
-Bachelor’s degree and 8+ years relevant experience (software engineering, applied R&D, platform/DevSecOps, AI/ML engineering, cyber engineering). Additional years may substitute for degree. 
-Proven hands-on delivery: prototypes that ran, tools others used, automation others depended on. 
-Strong development skills; experience building LLM-enabled systems and/or multi-agent workflows. 
-Practical experience with Kubernetes and modern deployment/IaC practices; Terraform, K8s familiarity. 
-Must be a US Citizen with the ability to obtain and maintain a Secret clearance. 

​Preferred Qualifications: 
-Experience productizing internal platforms (“paved roads”): templates, reference architectures, golden paths, and guardrails. 
-GitOps delivery experience (Argo CD/Flux or similar), and observability pipeline experience (OpenTelemetry or similar). 
-Workload identity / service-to-service auth experience (SPIFFE/SPIRE, mTLS, policy enforcement). 
-Familiarity with research methodologies, with a focus on establishing validation of techniques (e.g., F1 scores, ROC/AUC, correlation methods). 
-Ability to obtain and maintain a TS/SCI clearance. 
 

If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo — because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 — and moving faster than anyone else dares.

Original Posting:

February 6, 2026

Pay Range:

Pay Range $107,900.00 - $195,050.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.