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.
Core values that hold us all together:
PRACTICE CUSTOMER OBSESSION. Focus on the customer journey and work backwards. Strive to deliver customer value and continuously solve customer problems. Listen to customer feedback, act, and iterate to improve customer experience.
LEAD. Take ownership and lead through action. Think and act on behalf of the entire company to build long-term value across team boundaries.
DEVELOP AND HIRE THE BEST. Strive to raise the performance bar by continuously investing in yourself, the team and by hiring the best possible candidates for every position. Drive towards personal development and professional growth, and mentor others to raise the collective bar.
EXPECT AND ADVOCATE CHANGE. Strive to innovate and accept the inevitable change that comes with innovation. Constantly welcome new ideas and opinions. Share insights responsibly with unwavering openness, honesty, and respect. Once a path is chosen, be ready to disagree and commit to a direction.
What does AI Enabler Team do?
In the AI Enabler team, our day is usually full of R&D challenges. Have you ever encountered a situation where you need to expand your AI infrastructure so that the applications can automatically pick the right large language models (LLMs) that are both more cost-efficient and better performing? Most of us probably do nowadays, or at least understand the complexity of making such decisions while keeping track of our cloud budget.
One of the team’s responsibilities is ensuring that whenever a customer makes AI-related decisions regarding their K8s infrastructure, they are implemented automatically without unnecessary costs or hassle. This is just one small piece of a bigger puzzle. To get into a more detailed perspective, ask yourself the following questions:
How often do you use LLMs?
What is the least expensive LLM you can pick for a given prompt without degrading the quality of the response?
How much do your applications cost per 1 million tokens and how can you improve it?
Which API keys have the biggest waste?
How can you improve your frequently running prompt to use fewer tokens?
What is fine-tuning and how to do it efficiently?
What is a transformer?
These are just several of the many questions that are part of the daily work of this team.
Being a part of this team would involve design and decision-making end-to-end while collaborating with colleagues from other teams. Cast AI, being a technical product, encourages not only coding something as written in the JIRA ticket but also coming up with new features and potential solutions to customers’ problems. Given that the team is working on a technical greenfield project, you will have the opportunity to impact it in many ways positively.

RESPONSIBILITIES FOR THE ROLE:

Evaluate and Analyze LLM performance
Fine-Tune LLMs
Optimize AI Models for Cost Efficiency
Develop and implement data science solutions
Architect and build inference and training pipelines, directly contributing through hands-on design, model training pipeline, and deployment strategies
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