
Senior Machine Learning Operations Specialist
2 days ago
Are you ready to take your machine learning skills to the next level? As an experienced Machine Learning Operations engineer, you will be responsible for deploying, monitoring, and scaling machine learning models on cloud platforms such as AWS or GCP.
Key Responsibilities:
- Deploy and manage ML models using SageMaker, Vertex AI, and other cloud-based tools
- Build and maintain continuous integration/continuous deployment pipelines for machine learning workflows using GitHub Actions, Jenkins, or cloud-native tools
- Containerize and orchestrate workloads with Docker and Kubernetes
- Implement model registries, feature stores, and observability using MLflow, Feast, and Prometheus/Grafana
- Collaborate with data scientists to operationalize machine learning models for personalization, recommendations, and NLP
Requirements:
- 2+ years of experience working with ML Ops and DS
- Proficient in Python, ML frameworks (Scikit-learn, TensorFlow, PyTorch), and data pipelines (Airflow, Spark, SQL)
- Bonus: experience with real-time ML serving, A/B testing, or media/recommender systems
Nice-to-Have:
- Experience with subscription, media, or recommender systems
- Knowledge of experiment design and causal inference
- Exposure to real-time ML serving (KFServing, Seldon, Ray Serve)
Your Career Advantages:
- Grow your expertise in machine learning operations
- Join a community of passionate professionals who drive innovation
- Enjoy a dynamic and fast-paced work environment
Additional Benefits:
- We offer competitive compensation packages
- A comprehensive benefits program, including health insurance and retirement savings plans
- Ongoing professional development opportunities
You Will Thrive Here If:
- You have a passion for machine learning and its applications
- You are driven by results and enjoy taking calculated risks
- You thrive in a collaborative environment where teamwork is essential
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