Job description
Join our customer’s team as an Applied AI Developer and work at the cutting edge of machine learning and generative AI. You will design, build, and deploy innovative AI solutions using state-of-the-art technologies such as LLMs and advanced NLP, while working closely with cross-functional teams. This unique opportunity allows you to deliver impactful solutions on massive datasets within a high-caliber, remote-first environment.
Key Responsibilities:
- Build, refine, and utilize advanced ML engineering platforms and reusable components to deliver scalable AI solutions.
- Implement ML Ops processes, track model KPIs, monitor drift, and establish robust feedback loops for continuous improvement.
- Deploy and operationalize deep learning models, with a focus on LLMs and generative AI, ensuring reliability and performance at scale.
- Design and orchestrate model pipelines, including feature engineering, inferencing, and continuous training, to meet strict SLAs.
- Collaborate with client-facing teams to understand high-level business contexts and translate them into technical requirements.
- Write production-ready code with a focus on testability, maintainability, and handling edge cases and errors gracefully.
- Actively participate in agile ceremonies, communicate progress effectively, and adhere to best practices for architecture, design, and code quality.
Qualifications:
- Bachelor’s or Master’s degree in Computer Science or a related field from a top-tier university.
- 4+ years of hands-on experience in machine learning, deep learning, and fine-tuning models (LLMs).
- Expert-level proficiency in Python; experience with backend API design and vector databases.
- Solid understanding of ML Ops, including measuring and tracking model performance, and MLFlow.
- Demonstrated experience in NLP, generative AI, and deploying real-time model predictions.
- Strong communication skills—both written and verbal—are essential for cross-functional collaboration.
- Experience with ML frameworks such as Keras and HuggingFace.
Preferred Qualifications:
- Familiarity with DevOps practices, CI/CD pipelines, cloud architecture, and data security.
- Experience in data engineering for big data systems and knowledge of PySpark or Scala.
- Background in computer vision and implementing end-to-end feature engineering pipelines.
About RYZ Labs:
RYZ Labs is a startup studio built in 2021 by two lifelong entrepreneurs. The founders of RYZ have worked at some of the world’s largest tech companies and some of the most iconic consumer brands. They have lived and worked in Argentina for many years and have decades of experience in Latam. What brought them together is the passion for the early phases of company creation and the idea of attracting the brightest talents in order to build industry-defining companies in a post-pandemic world.
Our teams are remote and distributed throughout the US and Latam. They use the latest cutting-edge technologies in cloud computing to create applications that are scalable and resilient. We aim to provide diverse product solutions for different industries, planning to build a large number of startups in the upcoming years.
At RYZ, you will find yourself working with autonomy and efficiency, owning every step of your development. We provide an environment of opportunities, learning, growth, expansion, and challenging projects. You will deepen your experience while sharing and learning from a team of great professionals and specialists.
Our values and what to expect:
- Customer First Mentality - every decision we make should be made through the lens of the customer.
- Bias for Action - urgency is critical, expect that the timeline to get something done is accelerated.
- Ownership - step up if you see an opportunity to help, even if not your core responsibility.
- Humility and Respect - be willing to learn, be vulnerable, and treat everyone who interacts with RYZ with respect.
- Frugality - being frugal and cost-conscious helps us do more with less
- Deliver Impact - get things done in the most efficient way.
- Raise our Standards - always be looking to improve our processes, our team, and our expectations. The status quo is not good enough and never should be.
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