Senior Machine Learning Engineer
2 days ago
Prior experience working with Machine Learning Operations and Data Science is essential.
Responsibilities include deploying, monitoring, and scaling machine learning models on cloud platforms such as AWS or GCP.
Build and maintain continuous integration and deployment pipelines for machine learning workflows using tools like GitHub Actions or Jenkins.
Containerize and orchestrate workloads with Docker and Kubernetes, manage infrastructure via Terraform or CloudFormation.
Implement model registries, feature stores, and observability using MLflow, Feast, Prometheus, or Grafana.
Collaborate with data scientists to operationalize machine learning models for personalization, recommendations, natural language processing.
Proficiency in Python and machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch is required.
Data pipeline experience with Airflow, Spark, or SQL is also necessary.
Bonus skills include real-time machine learning serving, A/B testing, or media/recommender systems.
Required Skills & Qualifications:- Cloud Platforms: Experience with AWS (SageMaker, EKS, Lambda) or GCP (Vertex AI, GKE, Cloud Functions)
- Continuous Integration/Deployment: Knowledge of GitHub Actions, Jenkins, or cloud-native tools
- Containerization & Orchestration: Proficiency in Docker and Kubernetes
- Infrastructure Management: Experience with Terraform, CloudFormation, or similar tools
- Model Registries & Feature Stores: Knowledge of MLflow, Feast, or similar technologies
- Observability: Familiarity with Prometheus, Grafana, or similar tools
- Machine Learning Frameworks: Proficiency in Python and Scikit-learn, TensorFlow, or PyTorch
- Data Pipelines: Experience with Airflow, Spark, or SQL
- Real-Time Machine Learning Serving: Knowledge of KFServing, Seldon, or Ray Serve
Benefits:
- Collaborative environment with experienced professionals
- Opportunities for growth and development
- Competitive compensation package
Education: Btech or Mtech degree is preferred
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