MLOps Engineer

Your Role

• Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment.
• Collaborate with data scientists and software engineers to operationalize ML models using serving frameworks (TensorFlow Serving, TorchServe) and MLOps tools.
• Develop and maintain CI/CD pipelines for ML workflows.
• Implement monitoring and logging solutions for ML models with experience in ML model serving frameworks (TensorFlow Serving, TorchServe).
• Optimize ML infrastructure for performance, scalability, and cost-efficiency.

Your Profile

• Strong programming skills in Python (5+ years), with experience in ML frameworks and understanding of ML-specific testing and validation techniques.
• Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes); knowledge of data and model versioning techniques.
• Proficiency in cloud platforms (AWS) and their ML-specific services with at least 2-3 years of experience.
• Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow, etc.).
• Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack) and knowledge of distributed training techniques.

What You'll Love About Working Here

• Flexible work arrangements with support for hybrid mode to maintain a healthy work-life balance.
• Focus on career growth and professional development with opportunities for exploration.
• Access to certifications and training programs in the latest technologies such as MLOps and Machine Learning.