Job Description
DevOps
Build and maintain CI/CD pipelines to streamline development, testing, and deployment processes
Support infrastructure provisioning and management using Infrastructure as Code (IaC) tools and practices
Monitor system performance, optimizes resource utilization, and ensures reliable delivery for cloud-based applications and data platforms
Develop and enhances scalable solutions for data processing, analytics, and cloud services, leveraging tools like Azure, GCP, Snowflake, Hadoop, and MongoDB
Implement robust security measures for cloud environments, ensuring compliance with industry standards and best practices
Collaborate with cross-functional teams to design, deploy, and maintain cloud-native applications and data pipelines
Troubleshoot and resolves issues related to infrastructure, applications, and data platforms to ensure high availability and performance
- Use AI Ops to enable predictive alerting, anomaly detection, and self-healing systems
- Design, develop, and manage data ingestion, transformation, and storage pipelines across multiple systems and sources.
- Build and optimize data models, ETL processes, and data integration frameworks to support analytical workloads.
- Manage structured and unstructured data across on-premise and cloud-based environments
- Implement data governance, security, and quality frameworks to ensure data accuracy and compliance.
- Collaborate with data scientists, analysts, and application teams to deliver reliable, high-quality data solutions.
- Monitor and optimize data workflows for performance, scalability, and cost efficiency.
- Maintain documentation, metadata, and lineage for all data assets and processes.
- Build and manage AI-ready data infrastructure to support machine learning, LLMs, and advanced analytics.
- Design and maintain feature stores and data pipelines for model training and real-time inference.
- Implement and manage vector databases, embedding pipelines, and retrieval-augmented generation (RAG) frameworks for generative-AI applications.
- Automate data engineering workflows using AI-based orchestration and quality monitoring tools.
- Collaborate with MLOps and platform engineering teams to deploy and monitor AI models in production.
- Apply synthetic data generation and anonymization techniques to expand training datasets while maintaining privacy.
Qualifications
BE
Range Of Year Experience-Min Year
3
Range Of Year Experience-Max Year
5
