[Remote] Lead DevOps/AIOps Engineer

Note: The job is a remote job and is open to candidates in USA. Blend360 is a data, AI, and marketing consulting firm that helps organizations use data, technology, and expertise to address complex business challenges. The Lead DevOps/MLOps Engineer will architect, automate, and operationalize cloud-based data and AI platforms on Google Cloud Platform while establishing DevOps and MLOps practices, leading technical solutions, and mentoring engineers.

Responsibilities

• Lead the design and implementation of cloud-native DevOps and MLOps architectures on GCP
• Build and optimize CI/CD pipelines for data, ML, and application workloads
• Develop infrastructure-as-code using tools such as Terraform and establish repeatable deployment patterns
• Architect and operationalize data platforms leveraging BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataproc, and Cloud Composer
• Build MLOps capabilities supporting the full ML lifecycle, including model development, deployment, monitoring, versioning, and retraining
• Establish observability across data and ML platforms, including logging, monitoring, alerting, pipeline health, data quality, and model performance
• Implement secure, scalable cloud infrastructure using GCP IAM, networking, secrets management, and appropriate security controls
• Partner with Data Engineers, ML Engineers, Architects, and client stakeholders to translate business requirements into production-ready technical solutions
• Establish engineering standards around deployment automation, testing, environment management, reliability, and operational excellence
• Troubleshoot complex production issues and drive root-cause analysis and long-term remediation
• Mentor engineers and serve as a technical leader across DevOps, cloud, data, and MLOps initiatives
• Evaluate emerging GCP and AI technologies and determine where they can create meaningful business or engineering value

Skills

• * 7+ years of experience in DevOps, cloud engineering, platform engineering, MLOps, or a related discipline
• * Strong hands-on experience with Google Cloud Platform, particularly BigQuery and cloud-native data services
• * Experience designing and implementing end-to-end data platforms on GCP
• * Strong understanding of BigQuery architecture, performance optimization, data ingestion, partitioning, clustering, and data security
• * Experience with CI/CD, Git, automated testing, containerization, and Kubernetes/GKE
• * Strong Infrastructure-as-Code experience, preferably Terraform
• * Experience with Vertex AI and/or production ML platforms, including model deployment and monitoring
• * Experience with orchestration and data processing technologies such as Cloud Composer/Airflow, Dataflow, Dataproc/Spark, and Pub/Sub
• * Strong understanding of observability, reliability engineering, monitoring, logging, and alerting
• * Proficiency with scripting/programming languages such as Python and/or Bash
• * Strong understanding of cloud security, IAM, networking, secrets management, and enterprise governance
• * Ability to operate at both the architectural and hands-on engineering levels
• * Excellent communication skills and the ability to work effectively with both technical teams and senior client stakeholders
• * Experience with Vertex AI, MLflow, Kubeflow, or other MLOps platforms
• * Experience implementing GenAI/LLM solutions in production
• * Experience with Docker and Kubernetes/GKE in enterprise environments
• * Familiarity with data quality, data lineage, metadata management, and semantic data layers
• * Experience with multiple cloud platforms, particularly AWS or Azure
• * Experience working in a consulting or professional services environment

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