MLOps Engineer CAPGEMINI TECHNOLOGY SERVICES INDIA LIMITED

  • company name CAPGEMINI TECHNOLOGY SERVICES INDIA LIMITED
  • working location Office Location
  • job type Full Time

Experience: 6 - 6 years required

Pay: INR 1400000 - INR 2600000 /year

Type: Full Time

Location: Bangalore

Skills: Artifactory, gitlab, Git flow, MLOps

About CAPGEMINI TECHNOLOGY SERVICES INDIA LIMITED

Job Description

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, serving frameworks (TensorFlow Serving, Torch Serve) and experience with ML'Ops 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, Torch Serve)
  • Optimize ML infrastructure for performance, scalability, and cost-efficiency 

Your Profile

  • Strong programming skills in Python, with 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
  • Strong understanding of DevOps practices and tools (GitLab, Artifactory, Git flow 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 recognize 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 programs in the latest technologies such as MLOps, Machine Learning