We’re looking for a talented Full-Stack Engineer with plenty of hands-on experience to join our dynamic team, building a state-of-the-art platform for benchmarking, evaluating and monitoring LLM-based applications. You’ll design and implement scalable systems to support advanced evaluation workflows, under high-volume, low-latency performance requirements. This role offers a unique opportunity to work on the cutting edge of LLM/LLMOps systems in a fast-paced startup environment.

What you will do

• Design, develop and deploy distributed backend systems in Python, onto AWS cloud.
• Design and build well-optimized UIs using React, TypeScript, and modern frontend tools, while collaborating with product and design to create great user experiences. 
• Own features end-to-end: from ideation, architecture design, coding, integration, all the way to deployment and testing.
• Build, maintain and optimize our APIs (REST/SDK) for seamless platform functionality.
• Ensure system reliability through testing and CI/CD pipeline contributions.
• Collaborate with Product and Machine Learning Engineers to deliver innovative LLM evaluation tools.

Required experience:

• At least 3 years of full-stack experience, with Python (or other high-level programming languages, such as Java, Golang), React and TypeScript with amazing coding skills.
• Product-driven mindset focused on scalable solutions.
• Collaborative, adaptable mindset for a fast-moving startup (prior startup experience is valuable).
• Ability to own features end-to-end (from design to deployment).
• Experience with relational databases (e.g. PostgreSQL) and complex data migrations and message queue technologies (e.g. SQS, RabbitMQ, Kafka)
• Familiarity with AWS ecosystem (e.g. EKS, Queues, Redis, RDS, IAM) – or other cloud provider.
• Knowledge of REST/SDK API best practices development.

Nice to have:

• Familiarity with JS fundamentals, component-based architecture and modern frontend workflows.
• Proficiency in DevOps technologies (Docker, Kubernetes, Terraform, Monitoring and Observability Tools).
• Experience with LLM-based systems or MLOps.