
ML Ops
3 weeks ago
Responsibilities & Skills:
Deploy, monitor, and scale ML models on AWS (SageMaker, EKS, Lambda) or GCP (Vertex AI, GKE, Cloud Functions).
Build and maintain CI/CD pipelines for ML workflows using GitHub Actions / Jenkins / cloud-native tools.
Containerize and orchestrate workloads with Docker & Kubernetes; manage infra via Terraform/CloudFormation.
Implement model registries, feature stores, and observability using MLflow, Feast, Prometheus/Grafana.
Collaborate with data scientists to operationalize ML models for personalization, recommendations, NLP.
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 & causal inference.
Exposure to real-time ML serving (KFServing, Seldon, Ray Serve).
Education:
Btech /Mtech
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