Senior LLMOps Engineer, CAST AI
AI Enabler Team
Competitive salary (€6,500 - €9,000 gross, depending on the level of experience) with equity options
More information about location
Kubernetes automation platform
Job no longer available
Kubernetes automation platform
201-500 employees
Job no longer available
Competitive salary (€6,500 - €9,000 gross, depending on the level of experience) with equity options
More information about location
201-500 employees
Company mission
To pioneer a brighter future of cloud computing.
Role
Who you are
- Experience with designing a production-grade machine learning system
- Strong software engineering skills in Python
- Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines
- Experience with the design and implementation of efficient model training and inference pipelines end to end
- 5+ years of hands-on experience in Data Science and Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects
- You have to be physically in any of the European countries GMT 0 to GMT +3
- Strong English skills
- Strong verbal and written communication skills
- Ability to work independently and collaborate in a group
What the job involves
- 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
- 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
- Stay up to date with industry trends
- Here are some of the tools we use daily:
- Python
- ClickHouse and PostgreSQL for persistence
- GCP Pub/Sub for messaging
- GRPC for internal communication
- REST for public APIs
- Kubernetes, which our product is evolving around
- AWS, GCP, and Azure cloud providers, which are currently supported in our platform
- We use GitLab CI with ArgoCD as our GitOps CD engine
- Prometheus, Grafana, Loki, and Tempo for observability
Application process
- We usually respond within three days
Our take
Managing cloud deployments effectively to control costs has become a priority for organizations of all sizes. CAST AI drives cloud optimization and cost reduction by using AI and machine learning to analyze a company’s cloud environment to identify the resources required for maximum performance.
CAST AI’s platform supports all three major cloud service providers; Microsoft Azure, Google Cloud, and Amazon Web Services. When cloud providers have unused machines they are typically discounted by 60-70%, however, if AWS (for example) needs that machine back, they give little warning before reclaiming it. CAST AI is able to automatically detect this, and immediately replace it, meaning companies using its platform will never experience an outage of discounted machines.
As more enterprises embrace cloud technologies, CAST AI is a strong choice of provider as it provides customers with a unified, automated platform, making Infrastructure as a Service (IaaS) more efficient and budget-friendly. The company has seen considerable investment that will be spent on attracting new customers, as well as helping to expand the CAST AI range of products.
Steph
Company Specialist at Welcome to the Jungle
Insights
51% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Apr 2025
$108m
SERIES C
Nov 2023
$35m
SERIES B
Total funding: $180.7m
Company HQ
Downtown Miami, Miami, FL
Leadership
Yuri Frayman
(Co-Founder & CEO)Co-founder & former CEO of ZENEDGE, Frayman Group, Viewdle, and LegalKEY Technologies. Founder & former Executive Chairman of CUJO AI. Current Director of Blumarine Group, and is a Board Member/Advisor at Koop Technologies, Secerus, and Perthera AI.
Leon Kuperman
(Co-Founder & CTO)Co-founder & former CTO of ZENEDGE, Co-founder of HostenPCI, and Founder & previous CTO of Truition. Previous CTO & Technology Advisor at CUJO AI, Vice President of Security Products OCI at Oracle, and was the President & CTO of Bidz.com. Current Board Member for OXYGEN.
Laurent Gil
(Co-Founder & President)Co-founder & former CPO of ZENEDGE. Previous CEO of Viewdle, CFO of WiPlug, and Director of IdeaCapital. Worked as a Group Architect at Oracle, and was an Advisor for both We Heart It and Cel-Fi.
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