
ML Model Deployment Specialist
2 days ago
We are seeking an experienced Machine Learning Operations Engineer to join our team. The ideal candidate will have a strong foundation in machine learning operations, excellent problem-solving skills, and the ability to collaborate effectively with cross-functional teams.
Key Responsibilities:
- Deploy and scale ML models on cloud platforms
- Build and maintain CI/CD pipelines for ML workflows
- Containerize and orchestrate workloads with Docker and Kubernetes
- Implement model registries, feature stores, and observability
- Collaborate with data scientists to operationalize ML models
Desirable Skills:
- Experience with real-time ML serving, A/B testing, or media/recommender systems
- Knowledge of experiment design & causal inference
- Exposure to real-time ML serving (KFServing, Seldon, Ray Serve)
Job Description: As a seasoned Machine Learning Operations Engineer, you will be responsible for deploying, monitoring, and scaling ML models on cloud platforms such as AWS and GCP. Your expertise in building and maintaining CI/CD pipelines using GitHub Actions, Jenkins, and cloud-native tools will ensure seamless integration and efficient workflows.
You will containerize and orchestrate workloads with Docker and Kubernetes, managing infrastructure via Terraform and CloudFormation. Additionally, you will implement model registries, feature stores, and observability using MLflow, Feast, and Prometheus/Grafana.
Collaborating closely with data scientists, you will operationalize ML models for personalization, recommendations, and NLP applications. Proficiency in Python, ML frameworks (Scikit-learn, TensorFlow, PyTorch), and data pipelines (Airflow, Spark, SQL) is essential for this role.
Benefits:
- Opportunity to drive innovation and excellence in machine learning
- Collaborative and dynamic work environment
- Competitive salary and benefits package
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