MLOps & Big Data Engineer
2 weeks ago
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)-
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