Key Responsibilities
- Design build and maintain scalable ML pipelines for training testing and deployment
- Operationalize machine learning models using CI/CD best practices
- Collaborate with Data Scientists Data Engineers and DevOps teams to productionize models
- Monitor model performance drift and data quality in production
- Implement model versioning reproducibility and experiment tracking
- Automate workflows using orchestration tools
- Ensure security compliance and governance in ML lifecycle
- Optimize infrastructure for cost and performance
Key Skills & Requirements
Core Technical Skills
- Strong experience in Python and ML frameworks (TensorFlow PyTorch Scikit-learn)
- Hands-on experience with MLOps tools like MLflow Kubeflow Airflow
- Expertise in CI/CD pipelines (Jenkins GitHub Actions GitLab CI)
- Experience with containerization (Docker) and orchestration (Kubernetes)
- Knowledge of cloud platforms: AWS / Azure / GCP
- Familiarity with data engineering tools (Spark Kafka Databricks)
- Understanding of model monitoring drift detection and observability
Preferred Qualifications
- Experience working in a consulting or enterprise-scale environment
- Exposure to GenAI / LLMOps is a strong plus
- Knowledge of infrastructure-as-code (Terraform CloudFormation)
- Experience with feature stores (Feast Tecton)
Key Responsibilities Design build and maintain scalable ML pipelines for training testing and deployment Operationalize machine learning models using CI/CD best practices Collaborate with Data Scientists Data Engineers and DevOps teams to productionize models Monitor model performance drift and d...
Key Responsibilities
- Design build and maintain scalable ML pipelines for training testing and deployment
- Operationalize machine learning models using CI/CD best practices
- Collaborate with Data Scientists Data Engineers and DevOps teams to productionize models
- Monitor model performance drift and data quality in production
- Implement model versioning reproducibility and experiment tracking
- Automate workflows using orchestration tools
- Ensure security compliance and governance in ML lifecycle
- Optimize infrastructure for cost and performance
Key Skills & Requirements
Core Technical Skills
- Strong experience in Python and ML frameworks (TensorFlow PyTorch Scikit-learn)
- Hands-on experience with MLOps tools like MLflow Kubeflow Airflow
- Expertise in CI/CD pipelines (Jenkins GitHub Actions GitLab CI)
- Experience with containerization (Docker) and orchestration (Kubernetes)
- Knowledge of cloud platforms: AWS / Azure / GCP
- Familiarity with data engineering tools (Spark Kafka Databricks)
- Understanding of model monitoring drift detection and observability
Preferred Qualifications
- Experience working in a consulting or enterprise-scale environment
- Exposure to GenAI / LLMOps is a strong plus
- Knowledge of infrastructure-as-code (Terraform CloudFormation)
- Experience with feature stores (Feast Tecton)
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