Key Responsibilities :

- Design, develop, and deploy machine learning models and algorithms to solve complex business problems.

- Implement MLOps best practices to ensure robust, scalable, and maintainable machine learning pipelines.

- Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.

- Monitor and optimize the performance of machine learning models in production.

- Develop and maintain automated testing, continuous integration, and continuous deployment (CI/CD) pipelines for machine learning models.

- Ensure data quality and integrity throughout the machine learning lifecycle.

- Stay up-to-date with the latest advancements in machine learning, MLOps, and related technologies.

Qualifications :

- 4+ years of experience in machine learning and data science.

- Proven experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, TFX).

- Strong programming skills in Python and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn).

- Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).

- Solid understanding of software engineering principles, including version control (e.g., Git), testing, and code reviews.

- Excellent problem-solving skills and the ability to work independently and as part of a team.

- Strong communication skills and the ability to convey complex technical concepts to non-technical stakeholders.

Preferred Qualifications :

- Experience with big data technologies (e.g., Hadoop, Spark).

- Knowledge of data engineering and ETL processes.

- Familiarity with DevOps practices and tools.