AIOps Lead

Position Summary

The AIOps Engineering Lead is accountable for deployment and operational reliability and continuous deployment of enterprise AI systems spanning LLM workloads, agentic workflows, multimodal pipelines, and predictive ML systems across Google Cloud Platform (Google Cloud Platform) and Databricks. This role bridges AI Engineering patterns with run-time operations including observability, incident response, security, and automation.

Key Responsibilities

Own SLO/SLA posture for AI services and drive operational readiness for production deployments.

Lead incident management for AI workloads including triage, mitigation, RCA, and automation-based prevention.

Operationalize LLM and agentic AI systems with reliable, scalable Google Cloud Platform-native or Databricks deployment patterns.

Standardize automated ML/LLM pipelines for versioning, testing, and rollback strategies.

Establish full-stack observability covering data quality, drift, safety indicators, latency, errors, and cost.

Drive performance and cost optimization across GPU/CPU utilization, autoscaling, and throughput.

Enforce security, governance, and Responsible AI controls including IAM, secrets, lineage, and approvals.

Act as onsite technical lead coordinating with stakeholders and offshore engineering teams.

Create reusable templates, reference architectures, golden paths, and operational documentation.

Required Skills & Qualifications

Strong hands-on experience with Python, Docker, Kubernetes, and CI/CD for production systems.

Proven experience operating LLM/RAG and enterprise ML systems in production.

Strong in data engineering, schema governance, and batch/stream processing.

Deep experience on Google Cloud Platform including Vertex AI, Cloud Run, Pub/Sub, Cloud Build.

Experience with BigQuery, Databricks, and Cloud Composer (Airflow).

Strong security mindset covering IAM, secrets management, auditability, and compliance.

Excellent communication skills and ability to drive cross-functional execution onsite.

Preferred Qualifications

Experience with agentic frameworks such as Google ADK.

Contributions to internal platforms, reusable frameworks, or open-source tooling.