Senior AI/Cybersecurity Solutions Engineer

RCG•Suitland, MD

14h•$130,000 - $140,000•Onsite

About The Position

RCG is seeking a Senior AI/Cybersecurity Solutions Engineer to support a federal Security, Architecture, and Engineering (SAE) team in Suitland, MD. This is a hands-on senior engineering role for an experienced professional who combines generative and agentic AI engineering, cloud security, cybersecurity automation, and federal compliance expertise. The selected candidate will help drive an agentic AI security platform, develop and harden automation supporting NIST 800-53 security assessments and remediation workflows, and take ownership of technical workstreams from requirements and implementation through CI/CD, deployment, and evidence generation. The ideal candidate will be equally comfortable developing Python-based AI automation, working with AWS security services and IAM, implementing secure cloud solutions, and maintaining the technical documentation and project traceability required in an auditable federal environment.

Requirements

Bachelor’s degree in computer science, Cybersecurity, or a related technical discipline.
6+ years of cloud security engineering experience.
2+ years of production experience with generative AI/LLM technologies.
4+ years of experience working with AWS security services.
3+ years of experience supporting federal compliance frameworks such as NIST, FedRAMP, and FISMA.
2+ years of active Jira and Confluence experience in an Agile or technical delivery environment.
Expert-level Python development experience, including Python 3.11+, boto3, type hints, and robust error handling.
Advanced experience with Amazon Bedrock, including agents, action groups, Knowledge Bases, guardrails, prompt engineering, and RAG.
Expert knowledge of AWS security technologies, including Security Hub, GuardDuty, Inspector, Config, IAM, CloudTrail, and OIDC.
Advanced container security experience with ECR, ECS/EKS, image scanning, runtime monitoring, and hardening.
Expert knowledge of NIST SP 800-53 Rev. 5, including control families, assessment procedures, and evidence requirements.
Advanced experience with CI/CD and Infrastructure as Code technologies, including GitLab CI and CloudFormation and/or Terraform.
Advanced Git/version-control experience.
Demonstrated experience managing technical work in Jira and maintaining team documentation in Confluence.

Nice To Haves

Master’s degree in a related technical discipline.
3+ years of experience building production AI agents.
Experience supporting ATO and continuous monitoring activities.
Experience administering or configuring Jira boards and Confluence spaces.
Experience supporting federal IT security programs.
Familiarity with CISA Binding Operational Directives, including BOD 22-01 and BOD 26-04.
Experience with AWS Security Lake, OCSF, OSCAL machine-readable compliance, or Microsoft Sentinel integration.
Experience with AI red teaming or adversarial testing of LLM applications.
Familiarity with software supply-chain technologies and frameworks such as SLSA, Sigstore, and in-toto.
Certifications such as AWS Certified Security – Specialty, CISSP, CISM, CKS, or AWS machine learning certification are preferred.

Responsibilities

Design and implement Amazon Bedrock agent action groups, including OpenAPI schemas and Python Lambda execution layers.
Develop Retrieval-Augmented Generation (RAG) pipelines using Bedrock Knowledge Bases with sources such as NIST 800-53, CIS Benchmarks, organizational System Security Plans (SSPs), and threat intelligence.
Engineer prompts, guardrails, and input validation to improve AI accuracy, security, and resistance to prompt-injection attacks.
Evaluate and evolve foundation-model strategies as AI capabilities and platform requirements mature.
Develop AI-assisted capabilities supporting security assessment, remediation, hardening, alert triage, and compliance workflows.
Build automated NIST 800-53 control assessments and security evidence collection capabilities.
Develop remediation and hardening automation across AWS services, including S3, EC2, IAM, CloudTrail, ECS, and EKS.
Integrate live data from AWS Security Hub, GuardDuty, Inspector, and Config to support environment-aware security analysis and AI-assisted decision-making.
Support BOD 26-04 SLA logic, CISA KEV correlation, risk enrichment, and remediation SLA tracking.
Support security telemetry aggregation and integration with SIEM technologies, including Microsoft Sentinel and AWS Security Lake.
Support ECR vulnerability scanning, EKS runtime monitoring, and container configuration hardening.
Develop and analyze Software Bills of Materials (SBOMs) using formats such as SPDX and CycloneDX.
Correlate vulnerabilities with the CISA Known Exploited Vulnerabilities (KEV) catalog to support risk-based prioritization.
Perform dependency, provenance, and software supply-chain security checks, including detection of typosquatting and unsigned container images.
Maintain GitLab CI/CD pipelines supporting linting, testing, security scanning, builds, and deployments.
Implement secure OIDC-based AWS authentication.
Develop and manage Infrastructure as Code using CloudFormation and/or Terraform with least-privilege IAM.
Support deployment and rollout verification across application and Kubernetes/EKS environments.
Maintain strong Git practices, including branching, merge requests, and clean version-control history.
Manage engineering work through Jira, maintaining accurate tickets, epics, sub-tasks, workflows, status, and handoffs.
Maintain Confluence documentation, including architecture guides, runbooks, decision records, and onboarding materials.
Produce technical design documentation, evidence packages, and other artifacts capable of supporting federal security assessments and audits.
Maintain traceability between requirements, engineering work, code, deployments, and security evidence.
Take primary ownership of assigned technical workstreams and collaborate closely with cybersecurity and engineering team members.