Lead SRE

Lead Site Reliability Engineer - USA (B)

Country

United States

State

NA

Date Published

06-Oct-2026

Job

47644

Travel

You may occasionally be required to travel for business

Secondary locations

USA California - OFC at Home, USA Michigan - OFC at Home

Additional Locations

This role can be based remotely in United States

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BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage.

You build, operate, and continuously improve reliable, scalable, secure, and observable AI/ML and Generative AI platforms in production — supporting the full lifecycle from deployment and monitoring through incident management, optimization, and continuous improvement.

At this level you execute defined work with guidance and grow through review and mentorship.

Scope at this level:
Completes defined operations/observability tasks with guidance; learns AI failure modes and on-call basics.

Organizational impact expected:
Contributes reliable work to a project.

Core responsibilities for this role:

• Build and operate production-grade infrastructure and operational frameworks for LLM, GenAI, RAG, and Agentic AI applications.
• Define and defend SLOs/SLIs for agent and model behaviour — latency, availability, quality, and cost.
• Monitor production AI systems for performance, drift, hallucination rates, and quality regressions; act before customers are affected.
• Lead incident response for AI systems: detection, triage, mitigation, and blameless post-mortems.
• Build runbooks, automation, and self-healing for common AI operational issues; participate in on-call as required.
• Operate model/agent lifecycle in production: versioning, rollout, rollback, and safe promotion with evaluation gates.
• Manage LLM cost, capacity, and scaling; manage model serving/inference infrastructure.
• Maintain observability/tracing for agents, tools, and prompts (Langfuse, Open Telemetry, Open Search).
• Operate AI/ML workloads on IBM Z where required; harden the AMI Platform for reliability, scalability, and security.
• Partner with AI Engineers, Data Scientists, Dev Ops, and Security to embed reliability from design onward.

• SRE fundamentals — SLOs/SLIs, monitoring, alerting, incident management (depth scales with level).
• Linux, networking, distributed systems, troubleshooting, performance analysis.
• CI/CD and Dev Sec Ops  practices; containers/orchestration (Docker, Kubernetes, Open Shift); cloud platforms.
• Generative AI/LLM literacy; RAG/agents/tooling concepts as level requires.
• MLOps/LLMOps tooling and AI observability as level requires.
• Scripting/automation (Python, Bash); security-first, calm incident handling.
• Linux/scripting fundamentals; exposure to monitoring or cloud (project/internship OK).
• Curiosity about how LLM/agent systems fail in production.
• Calm, careful work under guidance; readiness for eventual on-call with support.
• Past experience:
  Contributed to monitoring, runbooks, or operational fixes within an existing workflow; basic exposure to cloud/containers.
• Delivery evidence:
  Reliable execution of defined ops tasks; clear notes; careful changes under guidance.
• Shared expectation:
  Completes defined tasks with guidance and checks work carefully.

• Agent frameworks and Agent Ops for multi-agent systems.
• LLM cost-optimization and…