- Experience in Gen AI, CI/CD pipelines, scripting languages, and a deep understanding of version control systems(e.g. Git), containerization (e.g. Docker), and continuous integration/deployment tools (e.g. Jenkins) third party integration is a plus, cloud computing platforms (e.g. AWS, GCP, Azure), Kubernetes and Kafka.
- Experience building production -grade ML pipelines.
- Proficient in Python and frameworks like Tensorflow, Keras, or PyTorch.
- Experience with cloud build, deployment, and orchestration tools
- Experience with MLOps tools such as MLFlow, Kubeflow, Weights & Biases, AWS Sagemaker, Vertex AI, DVC, Airflow, Prefect,
etc.,
- Experience in statistical modeling, machine learning, data mining, and unstructured data analytics.
- Understanding of ML Lifecycle, MLOps & Hands on experience to Productionize the ML Model
- Detail -oriented, with the ability to work both independently and collaboratively.
- Ability to work successfully with multi -functional teams, principals, and architects, across organizational boundaries and geographies.
- Equal comfort driving low -level technical implementation and high -level architecture evolution
- Experience working with data engineering pipelines