Senior Backend Engineer - Gen AI
New
Z
ZartisAI Transformation
European UnionFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSNode.jsKubernetesTypeScriptLLMGenerative AI
Requirements
- Extensive professional experience building backend applications with Node.js and TypeScript.
- Demonstrable experience designing, developing, and deploying Generative AI capabilities in production.
- Strong hands-on experience designing and operating solutions on AWS.
- Experience with serverless and container-based architectures, including EC2, ECS/EKS, Kubernetes, or equivalent AWS technologies.
- Hands-on experience with Amazon Bedrock and/or production LLM applications within the AWS ecosystem.
- Practical experience implementing LLM application patterns such as Retrieval-Augmented Generation (RAG), tool calling, context management, prompting, and knowledge indexing.
- Strong understanding of the challenges involved in running AI-powered applications in production.
- Proven ability to design scalable, secure, resilient, and maintainable backend systems.
- Experience making technical and architectural decisions and evaluating trade-offs in production environments.
- Strong communication and collaboration skills.
Responsibilities
- Design, develop, and deploy scalable backend applications using Node.js and TypeScript.
- Integrate Generative AI and LLM capabilities into production-grade applications and services.
- Design and operate cloud-native architectures on AWS, working across serverless and container-based environments.
- Build and deploy containerized workloads using technologies such as EC2, ECS/EKS, and Kubernetes.
- Build LLM-powered capabilities using Amazon Bedrock and patterns including RAG, tool calling, context management, prompting, and knowledge indexing.
- Design solutions that address the real-world challenges of production AI, including response quality, latency, cost efficiency, traceability, and observability.
- Apply engineering best practices around scalability, security, resilience, and maintainability.
- Contribute to backend and cloud architecture, making informed technical decisions and evaluating trade-offs.
- Collaborate with engineering and product stakeholders to take AI-powered capabilities from design through to reliable production delivery.
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