MLOps Engineer

Job Description

Job Title: MLOps Engineer (Remote) Experience: 5+ Years Location: Remote Type: Full-time About the Role: We are seeking an experienced MLOps Engineer to design, implement, and maintain scalable machine learning infrastructure and deployment pipelines. You will work closely with Data Scientists and DevOps teams to operationalize ML models, optimize performance, and ensure seamless CI/CD workflows in cloud environments (Azure ML/AWS/GCP). Key Responsibilities: ✔ ML Model Deployment: Containerize ML models using Docker and deploy on Kubernetes Build end-to-end ML deployment pipelines for TensorFlow/PyTorch models Integrate with Azure ML (or AWS SageMaker/GCP Vertex AI) ✔ CI/CD & Automation: Implement GitLab CI/CD pipelines for automated testing and deployment Manage version control using Git and enforce best practices ✔ Monitoring & Performance: Set up Prometheus + Grafana dashboards for model performance tracking Configure alerting systems for model drift, latency, and errors Optimize infrastructure for scalability and cost-efficiency ✔ Collaboration: Work with Data Scientists to productionize prototypes Document architecture and mentor junior engineers Skills & Qualifications: Must-Have: 5+ years in MLOps/DevOps, with 6+ years total experience Expertise in Docker, Kubernetes, CI/CD (GitLab CI/CD), Linux Strong Python scripting for automation (PySpark a plus) Hands-on with Azure ML (or AWS/GCP) for model deployment Experience with ML model monitoring (Prometheus, Grafana, ELK Stack) Nice-to-Have: Knowledge of MLflow, Kubeflow, or TF Serving Familiarity with NVIDIA Triton Inference Server Understanding of data pipelines (Airflow, Kafka) Why Join Us? 💻 100% Remote with flexible hours 🚀 Work on cutting-edge ML systems at scale 📈 Competitive salary + growth opportunities Show more Show less