Platform Engineer IV

An Insight Global client’s Infrastructure Intelligence and Analytics (IIA) team builds and operates the data platform and AI agent infrastructure that powers proactive network monitoring and autonomous investigation for network operations. As part of this group, the Platform Engineer IV is responsible for designing, building, and maintaining the AWS infrastructure that underpins the IIA Data Lake, agent runtime environments, CI/CD pipelines, and graph database systems. This role ensures production environments are stable, scalable, and secure while enabling data science and agentic AI workloads to operate reliably at scale.

Infrastructure and Data Lake

Design and manage AWS infrastructure for the IIA Data Lake including S3 storage, Glue data catalog, Athena query engine, and EMR compute clusters.

Manage cross-account connectivity, VPC networking, security groups, and IAM roles/policies to enable secure data flow between IIA, upstream data providers, and downstream consumers.

Build and maintain infrastructure for AI agent runtime environments, including compute resources for LangGraph agents deployed via LangSmith Deployments.

Support deployment and operation of AWS Neptune for the network topology graph (digital twin), including capacity planning, schema design support, and performance tuning.

Implement and manage infrastructure-as-code (Terraform, CloudFormation) for repeatable, auditable environment provisioning.

Manage IAM access key rotations, secrets management (AWS Secrets Manager, Delinea), and security compliance for on-premises and cloud integrations (e.g., Splunk Edge Processor).

CI/CD and Agent Deployments

Build and maintain CI/CD pipelines for AI agent deployments using GitLab CI/CD, Docker, and Artifactory.

Manage container lifecycle for agents deployed via LangSmith Deployments, including image builds, versioning, and rollback procedures.

Automate deployment workflows to enable rapid, reliable promotion of agents from development through production.

Coordinate with SpecGPT platform team on AI Gateway integration, cross-account deployment, and connectivity requirements.

Production Operations

Ensure production environment stability through monitoring, alerting, and incident response. Maintain SLAs for data pipeline availability and agent uptime.

Implement production monitoring and alerting for deployed agents (health checks, error rates, latency, resource utilization).

Coordinate with upstream data teams and platform teams (SpecGPT, Splunk, Public Cloud) on connectivity, firewall requests, and integration requirements.

Support data engineering team with infrastructure needs for new data source onboarding (storage provisioning, access controls, pipeline compute).

Perform other duties as required.

This role will pay between $60- $70 per hour.

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Strong communication skills with ability to explain infrastructure decisions to non-infrastructure stakeholders

Expert-level experience with AWS services: EC2, S3, IAM, VPC, Glue, Athena, EMR, Secrets Manager, CloudWatch

Strong experience with infrastructure-as-code (Terraform preferred, CloudFormation acceptable)

Experience managing cross-account AWS architectures, VPC peering, PrivateLink, and transit gateway configurations

Experience with IAM policy design, least-privilege access patterns, and service account management

Experience with containerization (Docker) and container orchestration

Experience with CI/CD pipelines (GitLab CI preferred)

Proficiency with Linux-based operating systems and shell scripting

Experience with monitoring and alerting tools (CloudWatch, Prometheus, Grafana, or similar)

Understanding of networking fundamentals: DNS, CIDR, NAT, firewalls, security groups

Demonstrated ability to work across teams and coordinate with external platform owners on connectivity and access requirements Experience with graph databases (AWS Neptune, Neo4j) including deployment, scaling, and operational management

Experience with Apache Kafka or similar streaming platforms

Experience with Apache Spark (Scala preferred) for distributed data processing

Experience with Airflow or similar workflow orchestration platforms

Experience in the telecommunications industry or other large-scale network operations environments

Familiarity with AI/ML infrastructure requirements (model serving, GPU/CPU compute, artifact management via MLflow or similar)

Experience with Splunk integration, particularly Edge Processor and MCP connectivity

AWS certifications (Solutions Architect, DevOps Engineer, or similar)