ML Ops

Prior ~2+ years of experience working with ML Ops & DS

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