SciDentAI

Responsibilities: Collaborate with data scientists, machine learning engineers, and software engineers to deploy ML models into production environments. Automate model deployment pipelines using CI/CD tools for continuous integration and delivery of machine learning models. Implement version control for ML models, datasets, and experiments (e.g., using DVC, MLflow, or similar tools). Develop and maintain infrastructure for model training, validation, and deployment using cloud platforms (AWS, Azure, GCP). Support scaling and monitoring of deployed ML models, ensuring they meet performance and reliability requirements in production. Set up and manage containerized environments for model deployment using tools such as Docker and orchestration platforms like Kubernetes. Develop and maintain monitoring tools to track model performance and data drift in production environments. Implement and automate workflows for data preprocessing, model training, testing, and versioning. Troubleshoot and resolve any issues related to model deployment, performance, or scalability. Collaborate with cross-functional teams to optimize model training pipelines and ensure seamless integration with production systems. Keep up to date with the latest tools, technologies, and best practices in MLOps and machine learning. Requirements: Experience: 0-2 years of experience in MLOps, DevOps, data engineering, or related fields. Familiarity with machine learning concepts and model deployment processes. Basic experience with cloud platforms such as AWS, Azure, or GCP. Experience with CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) for automating model deployment pipelines. Exposure to version control tools for ML (e.g., DVC, MLflow, Git). Proficiency in Python, including libraries like Pandas, NumPy, and Scikit-learn for ML-related tasks. Experience with containerization tools such as Docker and container orchestration platforms like Kubernetes. Familiarity with model monitoring tools and understanding of concepts like model drift and performance tracking. Knowledge of SQL and data pipeline technologies to handle and manipulate large datasets. Strong problem-solving and troubleshooting skills with a focus on scalable and efficient solutions. Good communication skills and the ability to collaborate effectively with cross- functional teams. Exposure to ML model deployment frameworks such as TensorFlow Serving, Seldon, or TorchServe. Familiarity with machine learning pipelines and orchestration tools like Kubeflow or Apache Airflow. Experience with data storage systems like Amazon S3, Google Cloud Storage, or databases like PostgreSQL, MongoDB, etc. Knowledge of model versioning and management platforms like MLflow, Weights & Biases, or ModelDB. Understanding of data security and privacy concerns in deploying machine learning models. Experience with performance monitoring tools like Prometheus, Grafana, or Datadog. Job Types: Full-time, Fresher Pay: ₹400,000.00 - ₹550,000.00 per year Benefits: Flexible schedule Health insurance Paid time off Work Location: In person