MLOps Engineer
Capgemini
MLOps Engineer
Job Description
MLOps Engineer
📋 Job Overview
The MLOps Engineer role at Capgemini involves designing and maintaining end-to-end ML pipelines for model training, evaluation, and deployment. You will collaborate with data scientists and engineers to operationalize models using tools like TensorFlow Serving and TorchServe, while implementing CI/CD pipelines and monitoring solutions. The position emphasizes optimizing ML infrastructure for performance, scalability, and cost-efficiency in a collaborative, global environment.
📍 Location: Bangalore
💼 Experience Level: Experienced Professionals
🏢 Business Unit: I and D Global Business Line
🎯 Key Responsibilities
- 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
✅ Required Qualifications
- Strong programming skills in Python (5+ years)
- Experience in ML frameworks
- Understanding of ML-specific testing and validation techniques
- Expertise in containerization technologies (Docker)
- Expertise in orchestration platforms (Kubernetes)
- Knowledge of data versioning and model versioning techniques
- Proficiency in cloud platform (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)
- Knowledge of distributed training techniques
🛠️ Required Skills
- Python
- ML frameworks
- ML-specific testing and validation techniques
- Docker
- Kubernetes
- Data versioning
- Model versioning
- AWS
- ML-specific services on AWS
- DevOps practices
- GitLab
- Artifactory
- Gitflow
- Prometheus
- Grafana
- ELK stack
- Distributed training techniques
- TensorFlow Serving
- TorchServe
- MLOps tools
- CI/CD pipelines
- Monitoring and logging solutions
🎁 Benefits & Perks
- Flexible work arrangements in hybrid mode
- Environment to maintain healthy work-life balance
- Focus on career growth and professional development
- Valuable certifications and training programmes in latest technologies such as MLOps and Machine Learning
Locations
- Bangalore, India
Salary
Estimated Salary Rangemedium confidence
2,500,000 - 4,200,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Pythonintermediate
- ML frameworksintermediate
- ML-specific testing and validation techniquesintermediate
- Dockerintermediate
- Kubernetesintermediate
- Data versioningintermediate
- Model versioningintermediate
- AWSintermediate
- ML-specific services on AWSintermediate
- DevOps practicesintermediate
- GitLabintermediate
- Artifactoryintermediate
- Gitflowintermediate
- Prometheusintermediate
- Grafanaintermediate
- ELK stackintermediate
- Distributed training techniquesintermediate
- TensorFlow Servingintermediate
- TorchServeintermediate
- MLOps toolsintermediate
- CI/CD pipelinesintermediate
- Monitoring and logging solutionsintermediate
Required Qualifications
- Strong programming skills in Python (5+ years) (experience)
- Experience in ML frameworks (experience)
- Understanding of ML-specific testing and validation techniques (experience)
- Expertise in containerization technologies (Docker) (experience)
- Expertise in orchestration platforms (Kubernetes) (experience)
- Knowledge of data versioning and model versioning techniques (experience)
- Proficiency in cloud platform (AWS) and their ML-specific services with at least 2-3 years of experience (experience)
- Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow etc.) (experience)
- Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack) (experience)
- Knowledge of distributed training techniques (experience)
Responsibilities
- 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
Benefits
- general: Flexible work arrangements in hybrid mode
- general: Environment to maintain healthy work-life balance
- general: Focus on career growth and professional development
- general: Valuable certifications and training programmes in latest technologies such as MLOps and Machine Learning
Tags & Categories
Answer 10 quick questions to check your fit for MLOps Engineer @ Capgemini.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs
MLOps Engineer
Capgemini
MLOps Engineer
Job Description
MLOps Engineer
📋 Job Overview
The MLOps Engineer role at Capgemini involves designing and maintaining end-to-end ML pipelines for model training, evaluation, and deployment. You will collaborate with data scientists and engineers to operationalize models using tools like TensorFlow Serving and TorchServe, while implementing CI/CD pipelines and monitoring solutions. The position emphasizes optimizing ML infrastructure for performance, scalability, and cost-efficiency in a collaborative, global environment.
📍 Location: Bangalore
💼 Experience Level: Experienced Professionals
🏢 Business Unit: I and D Global Business Line
🎯 Key Responsibilities
- 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
✅ Required Qualifications
- Strong programming skills in Python (5+ years)
- Experience in ML frameworks
- Understanding of ML-specific testing and validation techniques
- Expertise in containerization technologies (Docker)
- Expertise in orchestration platforms (Kubernetes)
- Knowledge of data versioning and model versioning techniques
- Proficiency in cloud platform (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)
- Knowledge of distributed training techniques
🛠️ Required Skills
- Python
- ML frameworks
- ML-specific testing and validation techniques
- Docker
- Kubernetes
- Data versioning
- Model versioning
- AWS
- ML-specific services on AWS
- DevOps practices
- GitLab
- Artifactory
- Gitflow
- Prometheus
- Grafana
- ELK stack
- Distributed training techniques
- TensorFlow Serving
- TorchServe
- MLOps tools
- CI/CD pipelines
- Monitoring and logging solutions
🎁 Benefits & Perks
- Flexible work arrangements in hybrid mode
- Environment to maintain healthy work-life balance
- Focus on career growth and professional development
- Valuable certifications and training programmes in latest technologies such as MLOps and Machine Learning
Locations
- Bangalore, India
Salary
Estimated Salary Rangemedium confidence
2,500,000 - 4,200,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Pythonintermediate
- ML frameworksintermediate
- ML-specific testing and validation techniquesintermediate
- Dockerintermediate
- Kubernetesintermediate
- Data versioningintermediate
- Model versioningintermediate
- AWSintermediate
- ML-specific services on AWSintermediate
- DevOps practicesintermediate
- GitLabintermediate
- Artifactoryintermediate
- Gitflowintermediate
- Prometheusintermediate
- Grafanaintermediate
- ELK stackintermediate
- Distributed training techniquesintermediate
- TensorFlow Servingintermediate
- TorchServeintermediate
- MLOps toolsintermediate
- CI/CD pipelinesintermediate
- Monitoring and logging solutionsintermediate
Required Qualifications
- Strong programming skills in Python (5+ years) (experience)
- Experience in ML frameworks (experience)
- Understanding of ML-specific testing and validation techniques (experience)
- Expertise in containerization technologies (Docker) (experience)
- Expertise in orchestration platforms (Kubernetes) (experience)
- Knowledge of data versioning and model versioning techniques (experience)
- Proficiency in cloud platform (AWS) and their ML-specific services with at least 2-3 years of experience (experience)
- Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow etc.) (experience)
- Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack) (experience)
- Knowledge of distributed training techniques (experience)
Responsibilities
- 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
Benefits
- general: Flexible work arrangements in hybrid mode
- general: Environment to maintain healthy work-life balance
- general: Focus on career growth and professional development
- general: Valuable certifications and training programmes in latest technologies such as MLOps and Machine Learning
Tags & Categories
Answer 10 quick questions to check your fit for MLOps Engineer @ Capgemini.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs