Lead DevOps/AIOps 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). 

We’re looking for someone who can move comfortably between architecture and hands-on engineering—designing scalable solutions, establishing DevOps and MLOps best practices, and helping engineering teams reliably move data and AI workloads into production. 

 

What you'll 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. 

• 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.