MLOps Engineer (DevOps / Platform / SRE background)

• Minimum 2–3 years of hands-on experience as a DevOps Engineer, Platform Engineer, SRE, or similar role.
• Strong expertise in Docker and Kubernetes.
• Hands-on experience with Infrastructure as Code, especially Terraform, and building CI/CD pipelines using GitLab CI, GitHub Actions, or Jenkins.
• Solid knowledge of at least one cloud platform: AWS, Azure, or GCP.
• Proficiency in scripting with Python, as well as Bash and HCL.
• Understanding of ML model lifecycle and hardware resource allocation (CPU/GPU/TPU) with initial hands-on AI/ML project experience.
• Fluent English communication skills (minimum B2 level).

Nice to have:

• Experience with model hosting and inference servers like KServe, vLLM, NVIDIA Triton Inference Server.
• Hands-on with MLOps platforms such as Kubeflow, MLflow, and DVC.
• Experience with monitoring and logging tools including Prometheus, Grafana, ELK Stack, and OpenTelemetry.
• Higher education degree, preferably in Computer Science or related fields.

Key Responsibilities:

• Build and maintain scalable, production-grade environments for training and serving ML models using Docker, Kubernetes, and cloud platforms.
• Design and automate the complete machine learning lifecycle including testing, training, validation, and production deployment.
• Define and manage cloud resources using Infrastructure as Code tools like Terraform or CloudFormation.
• Implement monitoring systems for model performance, detecting data/concept drift, and real-time alerting with tools like Prometheus and Grafana.
• Deploy tools for versioning models, experiments, and datasets, such as MLflow and DVC.
• Collaborate with ML Engineers and Data Scientists to define and enforce production best practices.