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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.
