Senior Backend Engineer — GenAI Group
Earnix is hiring a Senior Backend Engineer in Ramat Gan to design and build scalable Python microservices and AWS cloud infrastructure powering its GenAI products — LLM orchestration, agents platform, knowledge bases, and evaluation systems — as a senior technical voice owning features end-to-end.
- Compensation
- Not disclosed
- Location
- Ramat Gan, Tel Aviv District
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 5+ years of backend engineering experience building and operating production systems at scale
- Deep expertise in microservices architecture, API design, and distributed systems
- Cloud-native design experience on AWS (or GCP/Azure) using Kubernetes and Helm
- Proficiency in Python for building scalable backend services
- Hands-on experience integrating Generative AI into backend systems, including LLMs, knowledge bases, and agent frameworks
- Familiarity with tooling such as AWS Bedrock, LangChain/LangGraph, and vector databases
- Solid experience with SQL/NoSQL databases, caching layers, and message brokers such as PostgreSQL, Redis, Kafka, RabbitMQ, and Elasticsearch
- Strong command of observability practices with hands-on experience in tools such as Prometheus, Grafana, or Splunk
- Fluency in agile development and delivery tooling such as Jira, with a track record of shipping high-quality software
Nice to have
- Technical depth and pragmatism — making sound trade-offs under real-world constraints
- Clear communication with technical and non-technical partners
- Curiosity and adaptability — staying ahead of the fast-moving GenAI landscape
- Fostering collaboration and trust across product, engineering, and data science teams
Full description
We're looking for a hands-on Senior Backend Engineer to join the App Studio & Intelligence team within Earnix's GenAI Group. Our group builds the company's core Generative AI products — including a company-wide agents platform and customer-facing AI assistants — enabling teams across Earnix and our customers to design and deploy AI-driven solutions.
In this role, you'll design and build scalable microservices and cloud-native infrastructure at the heart of our GenAI applications, working with advanced AI capabilities such as knowledge bases, embeddings, LLM orchestration, agent frameworks, and evaluation systems. You'll be a senior technical voice on the team — owning complex features end-to-end, raising the engineering bar, and shaping how AI capabilities are delivered in production.
What You'll Do
Design, build, and own backend services and infrastructure for our GenAI applications — scalable, resilient, production-grade systems running on AWS.
Build the APIs, data pipelines, and backend integrations that power advanced AI capabilities, including knowledge bases, embeddings, LLM orchestration, agent workflows, and evaluation frameworks, with attention to performance, scalability, and security.
Lead complex design efforts — driving architecture discussions, design reviews, and technical decisions for significant areas of the system.
Collaborate closely with product, data science, and other engineering teams to translate business needs into robust backend solutions that enable AI-agent use cases.
Raise the engineering bar through code reviews, mentoring less experienced engineers, and championing best practices in quality, observability, and operational excellence.
Take ownership of production health, including monitoring, alerting, troubleshooting, and continuously improving system reliability.
Requirements
You'll do it using:
5+ years of backend engineering experience building and operating production systems at scale, with a strong sense of ownership from design through deployment and operations.
Deep expertise in microservices architecture, API design, and distributed systems, with cloud-native design experience on AWS (or GCP/Azure) using Kubernetes and Helm.
Proficiency in Python for building scalable backend services, with a focus on clean, production-grade code and seamless integration of AI components.
Hands-on experience integrating Generative AI into backend systems, including LLMs, knowledge bases, and agent frameworks, plus familiarity with tooling such as AWS Bedrock, LangChain/LangGraph and vector databases.
Solid experience with data and messaging technologies — SQL/NoSQL databases, caching layers, and message brokers such as PostgreSQL, Redis, Kafka, RabbitMQ, and Elasticsearch.