AI Platform Engineer Consultant
New
T
Total SuccessEnterprise AI platforms
Remote for US based candidates onlyFull-TimeMiddle
Salary140,000 - 170,000 USD per year
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Job Details
- Experience
- 5 or more years of experience in platform engineering, infrastructure engineering, backend engineering, or related technical roles
- Required Skills
- KubernetesCI/CD
Requirements
- Have 5 or more years of experience in platform engineering, infrastructure engineering, backend engineering, or related technical roles.
- Have experience supporting production AI, ML, automation, or agent-based systems.
- Have hands-on experience building policy systems, access-control layers, audit logging, workflow engines, or orchestration platforms.
- Have experience with identity or workload-identity systems such as SPIFFE/SPIRE, OAuth/OIDC, or Entra ID.
- Be familiar with durable workflow orchestration tools such as Temporal or similar frameworks.
- Have experience integrating enterprise middleware, message queues, APIs, ESBs, or legacy systems of record.
- Understand observability, distributed tracing, OpenTelemetry, logs, metrics, and system monitoring.
- Have experience with infrastructure as code, deployment automation, CI/CD, and containerized services.
- Have experience with AWS, Azure, Google Cloud, or another major cloud provider, and Kubernetes or a similar container orchestration platform.
- Be able to work from design specifications in greenfield or limited-code environments.
- Have strong engineering judgment and documentation skills, and be able to build secure, reliable, auditable systems.
- Have a bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Responsibilities
- Build core platform infrastructure for governed AI agent operations.
- Develop control-plane capabilities that separate proposed, approved, and executed actions.
- Implement policy enforcement, access control, and business-rule guardrails.
- Build governance workflows for configuration changes, approvals, versioning, rollback, and change tracking.
- Create audit logging for compliance, traceability, and evidence requirements.
- Develop connector frameworks with staging, retries, failure handling, circuit breakers, and safe writeback.
- Implement identity and workload-identity capabilities for agent registration, authentication, and authorization.
- Build observability instrumentation using tracing, spans, logs, and metrics.
- Support durable workflow orchestration and create synthetic test environments, validation tools, and regression harnesses.
- Support infrastructure provisioning, containerization, deployment automation, CI/CD, and enterprise integrations.
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