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, serving frameworks (TensorFlow Serving, TorchServe) and experience with MLOps tools.
Develop and maintain CI/CD pipelines for ML workflows.
Implement monitoring and logging solutions for ML models, experience with ML model serving frameworks (TensorFlow Serving, TorchServe).
Optimize ML infrastructure for performance, scalability, and cost-efficiency.
Your Profile
Strong programming skills in Python, with 5.5 to 14 years experience in ML frameworks; understanding of ML-specific testing and validation techniques.
Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes), Knowledge of data versioning and model versioning techniques.
Proficiency in cloud platform (AWS) and their ML-specific services with atleast 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 :
We recognise the significance of flexible work arrangements to provide support in hybrid mode, you will get an environment to maintain healthy work life balance.
Our focus will be your career growth & professional development to support you in exploring the world of opportunities.
Equip yourself with valuable certifications & training programmes in the latest technologies such as MLOps, Machine Learning
Capgemini Technology Services