Lead DevOpsAIOps Engineer

Blend360 is looking for a Lead DevOps / MLOps Engineer to help architect automate and operationalize modern cloud-based data and AI platforms for enterprise clients. 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). 

Were looking for someone who can move comfortably between architecture and hands-on engineeringdesigning scalable solutions establishing DevOps and MLOps best practices and helping engineering teams reliably move data and AI workloads into production. 

What youll do:

• 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. 

Qualifications :

• 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.  

Nice to Have 

• 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. 

Remote Work :

Yes

Employment Type :

Full-time