About Us:

At Calfus, we are known for delivering cutting-edge AI agents and products that transform businesses in ways previously unimaginable. We empower companies to harness the full potential of AI, unlocking opportunities they never imagined possible before the AI era. Our software engineering teams are highly valued by customers, whether start-ups or established enterprises, because we consistently deliver solutions that drive revenue growth. Our ERP solution teams have successfully implemented cloud solutions and developed tools that seamlessly integrate with ERP systems, reducing manual work so teams can focus on high-impact tasks.

None of this would be possible without talent like you! Our global teams thrive on collaboration, and we’re actively looking for skilled professionals to strengthen our in-house expertise and help us deliver exceptional AI, software engineering, and solutions using enterprise applications.

As one of the fastest-growing companies in our industry, we take pride in fostering a culture of innovation where new ideas are always welcomed—without hesitation. We are driven and expect the same dedication from our team members. Our speed, agility, and dedication set us apart, and we perform best when surrounded by high-energy, driven individuals.

To continue our rapid growth and deliver an even greater impact, we invite you to apply for our open positions and become part of our journey!

About the Role

As a Site Reliability Engineer (SRE) with a focus on Data Engineering for AI/ML, you will be responsible for the reliability, scalability, and performance of data systems and infrastructure that support machine learning workflows. You will work closely with data engineers, ML engineers, and infrastructure teams to ensure that our data pipelines, storage solutions, and computational resources are optimized and highly available, meeting the demanding requirements of AI/ML applications.

What You’ll Do:

Data Infrastructure Reliability:

• Build and maintain highly reliable and scalable data infrastructure, ensuring data availability and integrity across various AI/ML workflows.

• Design and optimize data pipelines for real-time and batch processing that support training and inference for machine learning models.

• Implement fault-tolerant and scalable data storage solutions, ensuring minimal downtime and performance bottlenecks.

Cloud and On-premises Infrastructure Management:

• Oversee cloud-based (AWS, GCP, Azure) and on-premises infrastructure used for data storage, data processing, and machine learning workloads.

• Ensure that data engineering platforms and tools (such as Apache Kafka, Spark, Hadoop, and similar technologies) are scalable and available to handle large-scale datasets and workloads.

• Manage and monitor data storage solutions such as S3, HDFS, BigQuery, Redshift, or similar technologies for performance and cost optimization.

• Monitoring and Automation:

• Implement comprehensive monitoring and alerting systems for all data pipelines, storage, and processing environments.

• Automate operational tasks to reduce manual intervention and improve efficiency, including pipeline orchestration and data flow management using tools like Apache Airflow, Kubeflow, or similar.

• Develop automated health checks and system recovery protocols to minimize downtime and system failures.

Collaboration with Data Science & Engineering Teams:

• Work closely with data engineers and ML teams to ensure seamless integration of data sources, models, and computational resources.

• Help deploy and maintain machine learning models in production environments by ensuring that necessary data transformations, model inputs, and outputs are handled reliably.

• Ensure data is accessible in the required formats, structures, and latencies for machine learning training and inference.

Incident Response and Troubleshooting:

• Lead incident response efforts to quickly resolve issues affecting the availability and performance of data pipelines and machine learning services.

• Identify root causes of system failures and implement measures to prevent recurrence, driving improvements in system design and operational processes.

• Conduct post-mortem analyses of incidents, create actionable reports, and implement preventative measures.

Security and Compliance:

• Ensure compliance with data privacy regulations (GDPR, CCPA, HIPAA, etc.) and secure management of sensitive data throughout its lifecycle.

• Implement and enforce security best practices, including encryption, access controls, and audit trails, to protect data in transit and at rest.

Optimization and Cost  Efficiency:

• Identify opportunities to optimize data processing and storage costs while maintaining high performance and availability.

• Leverage cloud cost management tools to optimize resource usage and ensure that data infrastructure is both cost-effective and performant.