Senior Software Engineer - Storage Optimization at Cast AI

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Full Time

Start Date

Immediate

Expiry Date

11 May, 26

Salary

9000.0

Posted On

10 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Software Engineering, Distributed Systems, Backend Development, GoLang, Kubernetes, Cloud Platforms, AWS, GCP, Azure, CI/CD, DevOps, Debugging, Optimization, Performance Tuning, Problem Solving, Collaboration

Industry

Software Development

Description

Why Cast AI? Cast AI is the leading Application Performance Automation (APA) platform, enabling customers to cut cloud costs, improve performance, and boost productivity – automatically. Built originally for Kubernetes, Cast AI goes beyond cost and observability by delivering real-time, autonomous optimization across any cloud environment. The platform continuously analyzes workloads, rightsizes resources, and rebalances clusters without manual intervention, ensuring applications run faster, more reliably, and more efficiently. Headquartered in Miami, Florida, Cast AI has employees in more than 32 countries worldwide and supports some of the world’s most innovative teams running their applications on all major cloud, hybrid, and on-premises environments. Over 2,100 companies already rely on Cast - from BMW and Akamai to Hugging Face and NielsenIQ. What’s next? Backed by our $108M Series C, we’re doubling down on making APA the new standard for DevOps and MLOps, and everything in between. About the role Storage Optimization automates Kubernetes storage management by dynamically rightsizing volumes, eliminating unused resources, and optimizing performance. This helps developers significantly reduce storage costs and improve application reliability without manual intervention. Here are some of the tools we use daily: Languages: GoLang (primary), Python (secondary for some cases) Cloud & Orchestration: Kubernetes, AWS, GCP, Azure Databases & Storage: PostgreSQL, Cloud Object Storage Messaging & APIs: GCP Pub/Sub, gRPC for internal communication, REST for public APIs Observability: Prometheus, Grafana, Loki, Tempo CI/CD & GitOps: GitLab CI with ArgoCD. Requirements: Strong software engineering skills with experience in distributed systems and backend development (ideally GoLang, but not a hard requirement as long as you’re willing to transition to it) Strong debugging, optimization, and performance-tuning skills Advanced level of experience with Kubernetes Deep understanding of cloud platforms: hands-on experience with cloud platforms like AWS, Google Cloud Platform (GCP), Microsoft Azure CI/CD and DevOps practices experience Strong English skills, both verbal and written Ability to work independently and collaboratively within a team Startup mindset: adaptable, proactive, and comfortable with ambiguity A proactive, problem-solving mindset with a "yes we can" attitude. Responsibilities: Optimize workload placement and cluster right-sizing Collect and analyze cluster data to drive scaling decisions Continuously improve consolidation and efficiency strategies Explore emerging cloud and hardware technologies (e.g., GPU sharing, new instance types) What’s in it for you? Competitive salary (€6,500 - €9,000 gross, depending on the level of experience) Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Equity options. Private health insurance. Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. #LI-Remote

Responsibilities

The role involves optimizing workload placement and cluster right-sizing while collecting and analyzing cluster data to drive scaling decisions. Additionally, the engineer will continuously improve consolidation and efficiency strategies.