Staff Engineer – Agentic AIOps (MCP, Context Engineering, LLM Automation)

Who are we? Equinix is the world’s digital infrastructure company®, shortening
the path to connectivity to enable the innovations that enrich our work, life
and planet. A place where bold ideas are welcomed, human connection is valued,
and everyone has the opportunity to shape their future. Help us challenge
assumptions, uncover bias, and remove barriers—because progress starts with
fresh ideas. You’ll find belonging, purpose, and a team that welcomes
you—because when you feel valued, you’re empowered to do your best work. Job
Summary We are looking for highly skilled Staff Engineer (AIOps) to design,
build, and scale the next generation of intelligent operational platforms for
our ecosystem. These engineers will work at the intersection of SRE, machine
learning, LLMs, observability, and automation, enabling predictive, autonomous
operations across a globally distributed environment. In this role, you will
architect and implement AIOps capabilities such as intelligent incident routing,
anomaly detection, operational copilots, ChatOps workflows, and automated
remediation. You will partner closely with SRE, platform engineering, service
management, and product teams to embed intelligence into operational workflows
and redefine how digital operations are run. This is a highly technical,
hands-on role requiring strong depth in applied ML/LLMs, operational systems,
automation frameworks, and observability data structures. Responsibilities AIOps
Platform & Intelligence Development Design and build AIOps models (LLMs or
classical ML) for anomaly detection, correlation, root-cause identification, and
intelligent event clustering. Develop operational copilots and chatbots capable
of responding to incidents, surfacing insights, and driving automation through
natural language. Build and maintain feature pipelines using telemetry, logs,
metrics, traces, and runtime state for operational intelligence use cases.
Implement use cases for predictive and preventive operations—capacity
forecasting, early warning systems, noisy neighbor detection, etc. LLM
Engineering & Applied AI Build knowledge-grounding systems for operational
copilots using runbooks, incident data, historical patterns, service maps, and
topology. Integrate LLM-based reasoning into observability and automation
platforms. Develop embeddings, retrieval systems (RAG), and intent
classification for operational queries. Automation & Intelligent Remediation
Build automated workflows for incident triage, diagnostics, collaboration, and
remediation. Architect closed-loop automation patterns connecting alerts →
insights → action → verification. Develop reusable automation modules with
integration to unified observability, cloud platforms, and orchestration
systems. Data, Observability & Integration Integrate AIOps models with
observability platforms (logs, metrics, traces, events, topology). Design
real-time inference systems for high-volume telemetry streams. Partner with SRE
and platform teams to ensure pipelines, data contracts, and instrumentation
support future AIOps workloads. Operational Excellence & Collaboration Work with
transformation teams to define AIOps onboarding patterns, enablement models, and
implementation guidelines. Drive AIOps adoption across multiple
products/platforms, ensuring reliability, scalability, and continuous
improvement. Participate in architecture reviews, data modeling discussions, and
SRE transformation initiatives. Qualifications 5+ years of experience in SRE,
platform engineering, ML engineering, data engineering, or AIOps-oriented roles.
Strong hands-on experience building ML or LLM-based systems with Python,
PyTorch/TensorFlow, or modern LLM frameworks. Experience building automation
workflows using tools like StackStorm, Rundeck, Airflow, Jenkins, or
cloud-native orchestration. Deep understanding of observability data (logs,
metrics, traces) and platforms like Datadog, Splunk, Prometheus, Grafana, ELK.
Experience designing and deploying RAG pipelines, embeddings, intent models, or
operational chatbots. Strong experience architecting streaming or event-driven
systems (Kafka, Kinesis, Pub/Sub). Familiarity with cloud-native systems,
Kubernetes, microservices, and modern deployment patterns. Excellent
problem-solving skills with the ability to translate operational challenges into
ML-based or automation-based solutions. Ability to collaborate across SRE,
platform, service management, and engineering teams. Must Have Skills Hands on
Experience using Claude Code, Codex or GitHub Copilot any one of them Good
Understanding of Context Engineering Understand the Agentic Harness frameworks
Have built one MCP server at least Equinix is committed to ensuring that our
employment process is open to all individuals, including those with a
disability. If you are a qualified candidate and need assistance or an
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Employment Opportunity and, in the U.S., an Affirmative Action employer. All
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an Equal Employment Opportunity and, in the U.S., an Affirmative Action
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without regard to unlawful consideration of race, color, religion, creed,
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