Designing role profiles for FedRAMP and government AI platforms

Hook: Why hiring for FedRAMP-authorized AI platforms is your top recruiting challenge in 2026

If your recruiting funnel still treats cloud-native AI operations and FedRAMP compliance as two separate hiring problems, you’re losing weeks and thousands of dollars per hire. After BigBear.ai’s late-2025 acquisition of a FedRAMP-authorized AI platform, organizations supporting government AI workloads must close a rare skills gap: engineers who can run high-performance AI services at scale and sustain strict federal security controls. This article gives hiring teams concrete role profiles and evaluation tactics for three mission-critical positions—Cloud Engineer, DevSecOps, and Compliance Lead—so you can shorten time-to-hire and onboard teams that keep FedRAMP-authorized AI platforms operational and audit-ready.

Context: What changed in 2025–2026 and why it matters

Late 2025 and early 2026 accelerated two parallel trends that reshape recruiting requirements for government AI platforms:

• Stronger AI governance and supply-chain scrutiny from federal stakeholders. Agencies now expect integrated AI risk-management practices aligned with NIST AI guidance and enhanced artifactability for models and data pipelines.
• FedRAMP authorization models shifted to faster continuous monitoring and automation-first evidence collection. That puts a premium on engineers who can instrument telemetry, automate control evidence, and operate secure infrastructure-as-code pipelines.

Together this means hiring for government cloud AI is no longer just about cloud ops or security—it's about combined domain knowledge: AI systems, security controls, and continuous compliance automation.

How to use this guide

This is a tactical hiring playbook. For each of the three roles you'll find:

• Role mission and core responsibilities
• Must-have and preferred skills (technical and behavioral)
• FedRAMP-specific tasks and artifacts they must manage
• Practical interview prompts, take-home assessments, and a scoring rubric
• Red flags and onboarding tips for the first 90 days

The organizational model

For FedRAMP-authorized AI platforms, place these roles in an integrated team model:

• Cloud Engineer – sits in Platform/Infrastructure and owns provisioning, secure baselines, and cloud-native performance for model runtime.
• DevSecOps Engineer – embedded in the delivery pipeline, owns IaC pipelines, container hardening, CI/CD security, and automated evidence collection.
• Compliance Lead – a hybrid policy/engineering role that manages the SSP, POA&M, audit responses, and liaises with AO/A&A stakeholders.

Role profile: FedRAMP-focused Cloud Engineer

Mission

Provide scalable, secure government-cloud infrastructure that runs AI workloads with constrained latency and documented security controls.

Core responsibilities

• Design and operate cloud environments in AWS GovCloud / Azure Government / Google Assured Workloads (as applicable).
• Implement secure networking, IAM, encryption-at-rest/in-transit, logging, and monitoring per FedRAMP High/Moderate baselines.
• Create and maintain hardened images, baseline AMIs/VMs, and container runtime policies.
• Collaborate with data scientists to provision GPU-backed clusters and model-serving endpoints with RBAC and quota controls.

Must-have skills & certifications

• Strong IaC experience: Terraform, CloudFormation, or Bicep with reusable module design — ask candidates for samples or practical modules (for example, candidates who can move from solo to studio often bring strong reusable IaC work).
• Deep familiarity with cloud-native networking, VPC, peering, transit gateways, and private endpoint configurations.
• Experience running GPU fleets (Kubernetes, EKS/GKE/AKS) and tuning autoscaling for inference/serving workloads.
• Practical knowledge of FedRAMP controls mapping to cloud services and evidence collection (SSP inputs such as network diagrams, control implementations).
• Preferred certifications: AWS Certified: Security – Specialty, Azure Security Engineer, or Google Professional Cloud Security Engineer. Public-sector experience is highly valued.

FedRAMP-specific tasks

• Produce configuration evidence for AC-2, SC-7, SC-13, CM-6, IA-2 and other applicable controls.
• Automate control evidence extraction (e.g., config snapshots, access logs) into your ConMon pipeline.
• Maintain inventory of authorized images and cryptographic key lifecycle documentation.

Interview & assessment

• Live architecture design: give a prompt to design a FedRAMP High network for an inference cluster (10–15 min whiteboard + 20 min critique). Evaluate VPC layout, isolation, monitoring, and cross-account access.
• Take-home lab (4–8 hours): deliver a Terraform module that provisions a private EKS cluster with private ECR, KMS-backed secrets, and an automated snapshot of CloudWatch logs. Score on modularity, security defaults, and testability.
• Artifact review: ask the candidate to review a mock SSP excerpt and identify gaps in control implementation and evidence mapping (30–45 min).

Red flags

• Unable to explain how to produce repeatable evidence for a specific control.
• Using undocumented or ad-hoc scripts rather than IaC for provisioning.

Role profile: DevSecOps Engineer for AI platforms

Mission

Secure the ML development lifecycle and delivery pipeline so model changes, deployments, and inferencing are auditable, reproducible, and continuously compliant.

Core responsibilities

• Build and secure CI/CD pipelines for model training, validation, and deployment; integrate SCA/DAST/SAST and model-risk checks where appropriate.
• Implement container image signing, SBOM generation, and policy-as-code (e.g., OPA/Gatekeeper) to enforce runtime controls.
• Instrument telemetry that maps to FedRAMP continuous monitoring requirements; automate evidence uploads to the compliance repository.
• Enable model provenance: artifact registries, lineage metadata, and access controls for model artifacts.

Must-have skills & certifications

• CI/CD tools: GitHub Actions, GitLab CI, Jenkins, or Tekton with experience hardening runners and secrets handling.
• Policy-as-code and admission controls: OPA/Gatekeeper, Kyverno, Sigstore for signing and verification.
• Container security and SBOM tooling: Trivy, Clair, Syft, Snyk. Experience generating and surfacing SBOMs for auditors — pipeline integrations and tooling patterns are discussed in broader CI/CD writeups such as model CI/CD.
• Strong scripting (Python, Go, Bash) to automate evidence collection and remediation.

FedRAMP-specific tasks

• Automate control evidence for CM-3 (configuration change control), SI-4 (malware protection), and AU-2/AU-6 (audit logging).
• Integrate security checks that produce deterministic, archivalable artifacts for the SSP and system inventory.

Interview & assessment

• Scenario interview: candidate walks through a pipeline that must prevent an unapproved model from being promoted. Ask them to diagram controls, gates, and signals.
• Practical assignment: provide a CI workflow with a deliberately insecure image; task is to add stages that enforce SBOM generation, vulnerability scanning, signature verification, and automatic rollbacks. Score on correctness, automation, and test coverage.
• Behavioral: examples of working with auditors and delivering missing evidence under tight timelines.

Red flags

• Overreliance on manual gating for control evidence.
• Lack of experience integrating security tooling into ML pipelines.

Role profile: Compliance Lead / FedRAMP Compliance Engineer

Mission

Own the system security plan (SSP), continuous monitoring program, POA&M lifecycle, and the organizational relationship with the Authorizing Official (AO) and Third-Party Assessment Organization (3PAO).

Core responsibilities

• Maintain and update the SSP and control implementation statements as the platform evolves.
• Drive remediation and risk acceptance workflows; keep the POA&M accurate and prioritized.
• Coordinate assessments, respond to artifact requests, and manage evidence timelines for reauthorization and continuous monitoring.
• Translate technical controls into operational processes and train engineering teams on evidence generation.

Must-have skills

• Practical FedRAMP experience: authored SSPs, participated in ATO or 3PAO assessments, managed POA&Ms.
• Solid understanding of NIST 800-53 controls and how they map to cloud services and DevSecOps workflows.
• Excellent documentation skills and ability to demand and extract reproducible evidence from engineering teams.
• Preferred: CISSP, CISM or equivalent governance certifications; hands-on experience with compliance tooling (e.g., Archer, ServiceNow GRC, or custom evidence repositories).

FedRAMP-specific tasks

• Own the evidence calendar and integrate automated evidence ingestion from the DevSecOps pipeline.
• Lead tabletop exercises for incident response and produce artifacts required by IR-4 and CP-2.
• Run continuous monitoring dashboards and escalate to AO for risk decisions.

Interview & assessment

• Ask for a redacted SSP they authored or an example POA&M remediation plan. Evaluate clarity, control mapping, and remediation rigor.
• Scenario: a new model inference service must be added to the authorized boundary within 30 days. Ask for a step-by-step plan that includes evidence, test steps, and risk mitigations.
• Behavioral: examples of negotiating risk acceptance with senior leadership or the AO.

Red flags

• Overly bureaucratic approach that delays delivery teams, or conversely, laxness that fails to produce evidence.
• Inability to prioritize POA&M items by risk and operational impact.

Evaluating candidates: an objective rubric

Use a consistent scoring framework for technical and role-fit interviews. Suggested weighting:

• Technical skill & assessments: 50%
• FedRAMP & compliance knowledge (artifact production): 25%
• Behavioral and communication (cross-team work & audit handling): 15%
• Culture fit and learning agility: 10%

Score technical tasks with concrete criteria: security defaults implemented, repeatability (IaC), evidence quality (artifact timestamps, descriptive metadata), and automation coverage. Penalize manual only solutions and undocumented workarounds.

Practical hiring playbook (30–90 days)

1. Prioritize hires based on critical path: start with the DevSecOps engineer to reduce audit friction, then Cloud Engineer to stabilize runtime, then Compliance Lead to finalize artifacts and AO coordination.
2. Day 1–30: run a focused onboarding sprint—SSP walkthrough, pipeline tour, access to evidence repo, and pair-programming sessions with existing engineers. Use structured onboarding kits and recruiter tool checklists such as a productivity & ergonomics kit for recruiters to keep ramping consistent.
3. Day 30–60: deliver two automation wins—automated config snapshot and a pipeline gate that produces signed SBOMs.
4. Day 60–90: execute a mock assessment to simulate a 3PAO evidence request and refine the POA&M; consider running the simulation like a migration exercise (platform migration playbooks show how to structure simulated moves and tests).

Tools, tech, and evidence pipelines you should require

• Infrastructure-as-code: Terraform modules with state locking and drift detection.
• Runtime orchestration: Kubernetes (EKS/GKE/AKS) with admission controllers and Pod Security Standards.
• Artifact traceability: container registries with signed images and SIGSTORE for provenance.
• SBOM & vulnerability scanning: Syft, Trivy, Snyk; automatic SBOM archival for each build — integrate these checks into model CI patterns like those outlined in broader CI/CD writeups (model CI/CD).
• Logging & monitoring: Cloud-native audit logs piped to long-term archive (S3/Blob) and SIEM for alerting.
• Compliance automation: evidence orchestration that maps pipeline artifacts to SSP control IDs.

Case study: rapid integration after an acquisition

In a scenario similar to BigBear.ai’s acquisition: a newly acquired FedRAMP-authorized AI platform arrives with an existing ATO and a partially documented SSP. The immediate hiring priorities should be:

• DevSecOps hire to canonicalize pipelines, sign images, and automate SBOMs (first 30 days).
• Cloud Engineer to consolidate environment baselines and lock down networking (days 30–60).
• Compliance Lead to reconcile the SSP with actual implementation and reduce POA&M backlog (days 30–90).

Within 90 days the team should reduce manual artifact requests by >70% by instrumenting evidence collection, which materially lowers risk during the next reauthorization or audit.

Actionable takeaways

• Hire for automation-first skills. FedRAMP in 2026 rewards teams that automate evidence and monitoring.
• Assess by artifact, not just answers. Require sample SSP excerpts, Terraform modules, and CI pipelines as part of candidate evaluation.
• Prioritize DevSecOps early. A hardened pipeline reduces audit friction and speeds onboarding of new models.
• Compensate for public-sector complexity. Look for candidates with experience in government clouds and a willingness to work with AO/3PAO stakeholders.
• Simulate audits during onboarding. Mock 3PAO requests expose process gaps early and build trust with compliance owners.

"In 2026, the margin between a sustainable FedRAMP AI operation and a fragile one is the degree to which engineers can produce repeatable, auditable artifacts on demand."

Final hiring checklist

• Role-specific take-home assessments ready and aligned to your production stack.
• Scoring rubric that includes artifact quality and automation level.
• Pre-approved candidate sourcing channels for cleared or public-sector experienced talent.
• Onboarding plan that delivers measurable ConMon automation wins in the first 90 days.

Call to action

If you’re scaling a FedRAMP-authorized AI platform, you don’t need generic cloud hires—you need specialists who can merge AI operations with continuous compliance. recruits.cloud helps teams source, evaluate, and onboard engineers with FedRAMP and government-cloud experience using role-specific labs and scoring templates based on the playbook above. Contact us to get a tailored hiring kit and a 30-day recruiting plan for your FedRAMP AI platform. For hiring operations and recruiter tooling, consider pairing your processes with practical recruiter kits and onboarding checklists (recruiter ergonomics kits) and mentoring/pricing guidance (mentoring pricing guides) to accelerate ramp.

Related Reading

• How Salary Transparency Laws Reshaped Hiring in 2026 (jobsnewshub)
• CI/CD for Generative Models and Complex Pipelines (challenges.pro)
• Productivity & Ergonomics Kit for Remote Recruiters (freejobsnetwork)
• Checklist for Launching a Low-Stress Wellness Podcast
• 3 Ways Influencers Can Monetize Tech Deals (Plus Email Templates to Promote the Mac mini Sale)
• Pop-Up Wellness Thrift Sale: A Step-by-Step Event Plan for January
• Cleaning and Sanitizing LEGO Sets: Safe Methods That Won’t Damage Collectibles
• Field Notes 2026: Portable Power & Solar Kits for Community Health Outreach