Lead Observability Engineer (New Relic)

EPAM Systems is a global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and startups. They have over thirty years of expertise in custom software, product, and platform engineering, enabling clients to become AI-Native enterprises.

• 6+ years of experience in observability or Site Reliability Engineering
• Hands-on experience consolidating observability/telemetry for AI agent estates in a production GenAI project, such as multi-tenant New Relic aggregation, cost/token attribution for GenAI workloads, or AgentCore GenAI Observability integration
• Expertise in New Relic or equivalent SaaS observability platforms operated as a multi-tenant consolidation layer
• Skills in cross-account/cross-tenant telemetry aggregation and reporting
• Familiarity with Technical/Business/Audit observability segregation model
• Proficiency in NRQL, dashboards-as-code, and alert-policy-as-code practices
• Knowledge of OpenTelemetry / ADOT consumption fundamentals

Nice to have:

• Familiarity with AWS Bedrock AgentCore observability (Transaction Search, GenAI Observability)
• Background in Kinesis Firehose metric/log forwarding pipelines (CloudWatch → New Relic)
• Knowledge of cost/token attribution modeling for GenAI workloads
• Skills in Crossplane-based provisioning of observability resources

• Own the multi-tenant New Relic consolidation layer aggregating telemetry across the agent estate
• Administer New Relic at scale including management account setup and cross-account configuration
• Build and maintain cross-account NRQL queries aggregating telemetry from multiple project tenants
• Develop dashboards-as-code and alert-policy-as-code using the Terraform New Relic provider and Crossplane
• Configure New Relic alert policies routed to Opsgenie, with future migration to ServiceNow
• Consume OpenTelemetry / ADOT data through OTLP and Firehose ingestion into New Relic
• Design metric dimensions for large agent estates ensuring stable, low-cardinality facets
• Maintain segregation across Technical, Business, and Audit observability models

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