Lead AI Ops Engineer (DevOps - GCP)

Lead DevOps / AIOps Engineer

Location: Remote (USA)

Term: 3 Months, extendable

Type: Contract

Hourly Rate: $70-$85/hr (Dependent on Experience)

**we will NOT be engaging with agencies or 3rd parties for this role. No c2c.**

About the Opportunity

Our client is an internationally awarded Data, AI, and Machine Learning consultancy that helps enterprise organizations leverage cloud technologies, advanced analytics, and artificial intelligence to solve complex business challenges. They partner with leading organizations to design and implement scalable, production-ready data and AI platforms that drive innovation and business value.

The team is seeking a Lead DevOps / AIOps Engineer to help architect, automate, and operationalize modern cloud-based data and AI platforms. This role sits at the intersection of cloud infrastructure, data engineering, machine learning, and software delivery, with a strong emphasis on Google Cloud Platform (GCP) .

This position is ideal for someone who enjoys both strategy and hands-on engineering, with the ability to design scalable cloud architectures, establish DevOps and AIOps best practices, and help teams successfully move data and AI solutions into production.

Responsibilities

• Lead the design and implementation of cloud-native DevOps and AIOps architectures on Google Cloud Platform (GCP).
• Build and optimize CI/CD pipelines supporting data, AI, and application workloads.
• Develop Infrastructure-as-Code solutions using Terraform and establish repeatable deployment standards.

• Architect and operationalize enterprise data platforms leveraging:

• BigQuery
• Cloud Storage
• Dataflow
• Pub/Sub
• Dataproc

• Cloud Composer

• Build AIOps capabilities supporting the full AI lifecycle, including model development, deployment, monitoring, versioning, and retraining.
• Establish observability across data and AI platforms, including logging, monitoring, alerting, pipeline health, data quality, and model performance tracking.
• Implement secure and scalable cloud infrastructure utilizing GCP IAM, networking, secrets management, and security best practices.
• Collaborate with Data Engineers, AI Engineers, Architects, and business stakeholders to deliver production-ready solutions.
• Define and promote engineering standards around automation, testing, environment management, reliability, and operational excellence.
• Troubleshoot complex production issues and lead root-cause analysis efforts.
• Mentor engineers and provide technical leadership across cloud, DevOps, data platform, and AIOps initiatives.
• Evaluate emerging cloud and AI technologies and recommend solutions that create measurable business value.

Required Qualifications

• 7+ years of experience in DevOps, Cloud Engineering, Platform Engineering, MLOps, or a closely related discipline.
• Strong hands-on experience with Google Cloud Platform (GCP), particularly BigQuery and cloud-native data services .
• Experience designing and implementing enterprise-scale data platforms on GCP.
• Strong understanding of BigQuery architecture, performance optimization, data ingestion, partitioning, clustering, and security best practices.

• Experience with:

• CI/CD pipelines
• Git-based workflows
• Automated testing
• Containerization technologies

• Kubernetes / Google Kubernetes Engine (GKE)

• Strong Infrastructure-as-Code experience, preferably with Terraform.
• Experience with Vertex AI and/or production machine learning platforms, including model deployment and monitoring.

• Hands-on experience with orchestration and data processing technologies, including:

• Cloud Composer / Airflow
• Dataflow
• Dataproc / Spark

• Pub/Sub

• Strong understanding of observability, reliability engineering, monitoring, logging, and alerting.
• Proficiency with Python and/or Bash scripting.
• Strong understanding of cloud security, IAM, networking, secrets management, and enterprise governance.
• Ability to operate at both the architectural and hands-on implementation levels.
• Excellent communication and stakeholder-management skills.

Preferred Qualifications

• Experience with Vertex AI, MLflow, Kubeflow, or other MLOps platforms.
• Experience implementing Generative AI (GenAI) or Large Language Model (LLM) solutions in production environments.
• Experience with Docker and Kubernetes/GKE in enterprise-scale deployments.

• Familiarity with:

• Data Qu…