- 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