ai platform engineer at agilify remote

Agilify is a minority, woman-owned boutique consultancy based in Baton Rouge, LA, growing its footprint across the Gulf South region. We deliver IT and business strategy, process optimization, technology implementation, and training services that drive measurable client outcomes. Our mission is to build a culture of inclusion, transparency, and accountability — creating value for both our team and our clients.

ROLE OVERVIEW

The AI Platform Engineer builds and hardens the core platform layers of an enterprise AI governance platform — access control, audit, orchestration, and connector infrastructure — that every AI agent operates within.

KEY RESPONSIBILITIES

- Build the core control-plane infrastructure enforcing separation between recommend, decide, and execute actions across all agents.<br/>- Implement business-rule/guard-rail engines, risk-model registries, and configuration-governance workflows, including dual approval, versioning, and rollback.<br/>- Build access-control and policy-enforcement layers (e.g., OPA/Cedar-style policy engines) scoped beyond native system-of-record roles.<br/>- Implement immutable, hash-chained audit logging that satisfies enterprise compliance evidence requirements.<br/>- Build the governed connector framework — staging, retry, and circuit-breaker logic — that mediates all writes back to systems of record.<br/>- Stand up identity and workload-identity infrastructure (e.g., SPIFFE, Entra ID federation) for agent registration and authentication.<br/>- Implement observability instrumentation (OpenTelemetry, tracing/spans) so every governed action is traceable end-to-end.<br/>- Build and maintain the synthetic/test data environment and scenario/regression harness used to validate the platform independent of production systems.<br/>- Collaborate with the Solutions Architect to keep the platform’s integration contract stable and backward-compatible as new agents onboard.<br/>- Support infrastructure provisioning and deployment automation, including containerization and CI/CD, for the platform’s services.

REQUIRED SKILLS & QUALIFICATIONS

- 5+ years in platform, infrastructure, or backend engineering roles, including production AI/ML systems.<br/>- Hands-on experience building policy/access-control systems, audit logging, or workflow/orchestration engines.<br/>- Experience with identity and workload-identity systems (SPIFFE/SPIRE, OAuth/OIDC, Entra ID, or similar).<br/>- Familiarity with durable workflow orchestration frameworks (e.g., Temporal or similar).<br/>- Experience integrating with enterprise middleware (ESBs, message queues) and legacy systems of record.<br/>- Strong grasp of observability tooling (OpenTelemetry, distributed tracing) and infrastructure-as-code practices.<br/>- Comfortable working from a design specification with limited reusable code (greenfield builds).<br/>- Experience with at least one major cloud provider (AWS, Azure, or Google Cloud) and containerized deployments (Kubernetes or similar).<br/>- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

PREFERRED EXPERIENCE

- Experience building AI-governance or control-tower platforms specifically — approval routing, risk scoring, agent onboarding.<br/>- Familiarity with compliance-driven audit requirements (e.g., SOX ITGC, NERC-CIP, HIPAA, or similar record-keeping standards).<br/>- Experience building a reference/validation harness (e.g., a "crash-test-dummy" agent) used to onboard real integrators.<br/>- Background supporting critical-infrastructure or highly regulated environments.

Pay: $123,711.03 - $148,985.33 per year

Benefits:

- 401(k) matching<br/>- Dental insurance<br/>- Health insurance<br/>- Vision insurance

Experience:

- backend/platform/AI engineering: 5 years (Required)<br/>- building policy or access-control systems: 3 years (Required)<br/>- OAuth, OIDC, Entra ID, or SPIFFE: 2 years (Required)<br/>- integrating middleware or legacy systems: 2 years (Required)<br/>- OpenTelemetry or distributed tracing: 2 years (Required)<br/>- cloud and Kubernetes deployments: 2 years (Required)

Work Location: Remote

Location: Remote (Remote)

Remote: Yes