- Manage Data : Extract, clean, and structure both structured and unstructured data.

- Coordinate Pipelines : Utilize tools such as Airflow, Step Functions, or Azure Data Factory to orchestrate data workflows.

- Deploy Models : Develop, fine-tune, and deploy models using platforms like SageMaker, Azure ML, or Vertex AI.

- Scale Solutions : Leverage Spark or Databricks to handle large-scale data processing tasks.

- Automate Processes : Implement automation using tools like Docker, Kubernetes, CI/CD pipelines, MLFlow, Seldon, and Kubeflow.

- Collaborate Effectively : Work alongside engineers, architects, and business stakeholders to address and resolve real-world problems efficiently.

Requirements :

- 3+ years of hands-on experience in MLOps (4-5 years of overall software development experience).

- Extensive experience with at least one major cloud provider (AWS, Azure, or GCP).

- Proficiency in using Databricks, Spark, Python, SQL, TensorFlow, PyTorch, and Scikit-learn.

- Expertise in debugging Kubernetes and creating efficient Dockerfiles.

- Experience in prototyping with open-source tools and scaling solutions effectively.

- Strong analytical skills, humility, and a proactive approach to problem-solving.

- Experience with SageMaker, Azure ML, or Vertex AI in a production environment.

- Commitment to writing clean code, creating clear documentation, and maintaining concise pull request