Lead DevOps/AIOps Engineer (GCP)

Location: Columbia, MD, United States

Salary: USD 130,000–155,000 per year

Company

hirify.global is a data, AI, and marketing consulting firm helping enterprise and private equity-backed organizations create value through technology, analytics, and business execution.

What you will do

• Lead the design and implementation of cloud-native DevOps and MLOps architectures on Google Cloud Platform.
• Build CI/CD pipelines and infrastructure-as-code deployment patterns using Terraform for data, ML, and application workloads.
• Architect and operationalize data platforms using BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataproc, and Cloud Composer.
• Build MLOps capabilities covering model deployment, monitoring, versioning, retraining, and the wider ML lifecycle.
• Establish observability, reliability, security, monitoring, alerting, data quality, and model performance practices.
• Partner with engineers, architects, and client stakeholders, troubleshoot production issues, define engineering standards, and mentor engineers.

Requirements

• 7+ years of experience in DevOps, cloud engineering, platform engineering, MLOps, or a related discipline.
• Strong hands-on experience with GCP, especially BigQuery and cloud-native data services.
• Experience designing end-to-end GCP data platforms and understanding BigQuery performance, ingestion, partitioning, clustering, and security.
• Experience with CI/CD, Git, automated testing, containers, Kubernetes/GKE, Terraform, and scripting with Python and/or Bash.
• Experience with Vertex AI or production ML platforms, including model deployment and monitoring, plus orchestration and processing tools such as Airflow, Dataflow, Dataproc/Spark, and Pub/Sub.
• Strong knowledge of observability, cloud security, IAM, networking, secrets management, enterprise governance, and communication with technical teams and senior client stakeholders.

Nice to have

• Experience with Vertex AI, MLflow, Kubeflow, or other MLOps platforms.
• Production experience implementing GenAI or LLM solutions.
• Enterprise experience with Docker and Kubernetes/GKE.
• Knowledge of data quality, data lineage, metadata management, semantic data layers, AWS, or Azure.
• Experience in consulting or professional services.

Culture & Benefits

• Work with enterprise clients on data, AI, cloud, and marketing transformation initiatives.
• Collaborate across DevOps, data engineering, ML engineering, architecture, and client stakeholder groups.
• Operate at both architectural and hands-on engineering levels.