MLOps & Big Data Engineer

We are seeking a talented and driven MLOps and Big Data Engineer to join our growing team. The ideal candidate will have 2-5 years of experience in designing, developing, and maintaining MLOps pipelines and Big Data infrastructure. This role requires a hands-on engineer who can manage the end-to-end deployment of machine learning models and build scalable data processing pipelines. You will be responsible for ensuring the efficient and reliable delivery of AI solutions. Responsibilities : - Design, develop, and maintain MLOps pipelines for scalable AI model deployment. - Automate the model training, validation, and deployment processes. - Implement monitoring and logging solutions for deployed models. - Utilize MLOps tools such as MLflow, Kubeflow, SageMaker, and Airflow. - Manage the end-to-end deployment of machine learning models on both on-premise servers and AWS. - Ensure seamless integration of models into production environments. - Optimize model performance and scalability. - Build and manage Big Data infrastructure and pipelines for high-volume data processing. - Develop data ingestion, transformation, and storage solutions. - Utilize Big Data frameworks such as Spark, Hadoop, and Kafka. - Ensure data quality and reliability. - Work with Docker and Kubernetes for containerization and orchestration. - Use Terraform for infrastructure as code (IaC). - Manage and maintain cloud infrastructure on AWS. - Collaborate with data scientists, machine learning engineers, and software developers. - Communicate technical concepts effectively to both technical and non-technical stakeholders. - Participate in agile development processes. Required Skills & Experience : - 2-5 years of experience in MLOps and Big Data engineering. - Strong experience with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow). - Solid understanding of Big Data frameworks (Spark, Hadoop, Kafka). - Proven experience in deploying ML models in on-premise and cloud environments (AWS). - Hands-on experience with Docker and Kubernetes. - Experience with Terraform. - Experience with cloud computing platforms, specifically AWS. - Strong understanding of data engineering principles and practices. - Excellent problem-solving and debugging skills. - Strong communication and collaboration skills. - Experience with cloud-native technologies and architectures. - Knowledge of data warehousing and data lake concepts. - Experience with real-time data processing. - Familiarity with machine learning algorithms and techniques. - Experience with monitoring and logging tools. - Bachelor's degree in Computer Science, Data Science, or a related field. (ref:hirist.tech)

Location: mumbai, IN

Posted Date: 5/1/2025