[Hiring] Machine Learning Engineer @Capgemini

Role Description

As a Machine Learning Engineer, you will be responsible for developing, deploying, and maintaining predictive and optimization models that solve complex business challenges. You will work closely with data scientists, software engineers, and business stakeholders to build scalable ML solutions and bring them into production.

• Develop predictive and optimization machine learning models for business applications.
• Design, build, and maintain feature engineering and data processing pipelines.
• Deploy, monitor, and optimize machine learning models in production environments.
• Create and manage inference pipelines to serve machine learning predictions efficiently.
• Utilize MLflow for experiment tracking, model versioning, and lifecycle management.
• Integrate machine learning systems with enterprise applications and workflows.
• Collaborate with cross-functional teams to ensure scalable and reliable ML solutions.
• Follow MLOps best practices to improve model performance, reliability, and maintainability.

Qualifications

• 4+ years of experience in Machine Learning Engineering, Data Science, or related roles.
• Strong hands-on experience with production-grade machine learning deployments.
• Experience using MLflow for model management and experiment tracking.
• Expertise in developing time-series forecasting and predictive models.
• Strong understanding of feature engineering and inference pipeline development.
• Proficiency in building scalable machine learning solutions and integrating them into business applications.
• Excellent problem-solving, analytical, and communication skills.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.

Requirements

• Must-Have Skills:
• Python
• TensorFlow
• MLflow
• Model Deployment
• Machine Learning Model Development
• Feature Engineering
• Inference Pipelines
• Time-Series Modeling
• Nice-to-Have Skills:
• OR-Tools
• SimPy
• Forecasting
• Optimization Techniques
• MLOps Practices
• Docker / Kubernetes
• Cloud Platforms (AWS, Azure, or GCP)

Benefits

• We value flexibility and support our employees with remote work options and adaptable schedules to maintain a healthy work-life balance.
• Our inclusive culture brings together diverse professionals committed to growth, innovation, and excellence.
• You’ll have access to continuous learning opportunities and certifications in emerging technologies such as AI, Machine Learning, Cloud, and Data Engineering.