Key Skills & Requirements:
GenAI, ML and production-grade ML pipelines
CI/CD, Git, Docker, Jenkins and scripting languages
Cloud platforms – AWS / GCP / Azure
Kubernetes and Kafka
Python with TensorFlow, Keras or PyTorch
Cloud build, deployment and orchestration tools
MLOps tools such as MLflow, Kubeflow, Weights & Biases, AWS SageMaker, Vertex AI, DVC, Airflow, Prefect, etc.
Statistical modeling, machine learning, data mining and unstructured data analytics
Robust understanding of ML lifecycle and MLOps
Hands-on experience in productionizing ML models
Experience with data engineering pipelines
Third-party integration experience is an added advantage
Ability to work independently and collaboratively with cross-functional teams, architects and stakeholders across geographies
Comfortable with both low-level technical implementation and high-level architecture evolution