About The Role
The MLOps Engineer will build and operate the infrastructure that moves machine learning models from experimentation into reliable production systems. The role covers training and inference pipelines, model registries, feature and data workflows, deployment automation, and observability across cloud environments.
Working closely with ML engineers, data scientists, and platform engineers, this role will improve the speed and safety of model releases while maintaining production standards for latency, scalability, security, and reproducibility. The work will support real-time and batch workloads, including deep learning and LLM-based applications.
Key Responsibilities
- Build and maintain automated CI/CD pipelines for model training, validation, packaging, and deployment using Python, Docker, Kubernetes, and GitHub Actions or GitLab CI
- Design reproducible ML workflows with tools such as Kubeflow, Airflow, Argo Workflows, MLflow, or equivalent platforms
- Deploy and scale online and batch inference services across AWS, GCP, or Azure, optimizing compute utilization, latency, and reliability
- Implement model and data observability for drift, data quality, prediction performance, latency, error rates, and resource consumption using Prometheus, Grafana, or comparable tooling
- Manage model registries, feature stores, artifact repositories, and infrastructure-as-code with Terraform or CloudFormation
- Partner with ML and data science teams to standardize training environments, release processes, experiment tracking, and rollback procedures
- Strengthen production operations through automated testing, incident response, security controls, documentation, and service-level objectives
What We Are Looking For
- 3–8 years of experience in MLOps, machine learning engineering, platform engineering, DevOps, or a closely related discipline, including experience supporting production ML systems
- Strong Python and SQL skills, with practical experience building services, automation, data pipelines, and developer tooling
- Hands-on experience with Kubernetes, Docker, Linux, cloud infrastructure, and infrastructure-as-code tools such as Terraform
- Experience deploying and operating ML models using platforms or tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, Airflow, or Argo
- Solid understanding of ML lifecycle management, model versioning, feature and training data lineage, reproducibility, monitoring, and responsible rollback practices
- Bachelor’s degree in computer science, engineering, mathematics, statistics, or a related technical field; equivalent professional experience is also considered
- Bonus: experience with GPU scheduling, distributed training, Ray, Spark, Feast, LLM inference, model quantization, service meshes, or high-throughput real-time serving systems