We’re seeking a visionary Team Lead to lead a AI & backend-focused team building our next-generation Generative AI Platform. This platform will empower cross-functional teams across Earnix to design and deploy AI-driven solutions, leveraging cutting-edge advancements in generative AI, cloud-native architectures, and rapid innovation cycles. In this role, you’ll manage a high-performing backend team responsible for building scalable microservices and cloud-based infrastructure, while integrating advanced AI capabilities such as evaluation frameworks, RAG pipelines, knowledge base management, embeddings, LLMs, chatbot assistants, and agent orchestration.
What You’ll Do Lead and mentor a backend engineering team developing a scalable, cloud-native AI platform. You’ll foster a culture of technical excellence, collaboration, and ownership while ensuring delivery of robust, production-grade systems. Define the technical direction and architecture for backend services, focusing on microservices, distributed systems, and AWS-based infrastructure to support AI-driven applications. Integrate cutting-edge AI capabilities into the platform, including RAG pipelines, embeddings, LLM orchestration, and evaluation frameworks, ensuring performance, scalability, and security. Collaborate with product and engineering teams to translate business needs into backend solutions that enable AI-agent use cases and accelerate innovation across the organization. Drive agile processes and best practices , leveraging tools like Jira to manage sprints, track progress, and continuously improve team efficiency and delivery quality. Scale the team strategically , hiring and onboarding top talent while mentoring future leaders to support the growth of the Gen AI Platform group.
What You’ll Do Lead and mentor a backend engineering team developing a scalable, cloud-native AI platform. You’ll foster a culture of technical excellence, collaboration, and ownership while ensuring delivery of robust, production-grade systems. Define the technical direction and architecture for backend services, focusing on microservices, distributed systems, and AWS-based infrastructure to support AI-driven applications. Integrate cutting-edge AI capabilities into the platform, including RAG pipelines, embeddings, LLM orchestration, and evaluation frameworks, ensuring performance, scalability, and security. Collaborate with product and engineering teams to translate business needs into backend solutions that enable AI-agent use cases and accelerate innovation across the organization. Drive agile processes and best practices , leveraging tools like Jira to manage sprints, track progress, and continuously improve team efficiency and delivery quality. Scale the team strategically , hiring and onboarding top talent while mentoring future leaders to support the growth of the Gen AI Platform group.
Position Intro:
Earnix is the premier provider of mission-critical, cloud-based intelligent decisioning across pricing, rating, underwriting, and product personalization. These fully-integrated solutions provide ultra-fast ROI and are designed to transform how global insurers and banks are run by unlocking value across all facets of the business. Earnix has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.
Requirements:
You’ll Do It Using: 7+ years of backend engineering experience, including 2+ years in a leadership role, with a hands-on background that informs architectural decisions, complex design reviews, and engineering mentorship that drives high-quality delivery. Deep expertise in microservices architecture, distributed systems, and cloud-native design on platforms such as AWS, GCP, or Azure using Kubernetes and Helm— with a proven ability to build scalable, resilient, and maintainable systems at scale. Strong command of observability practices, including hands-on experience with tools such as Prometheus, Grafana, or Splunk to implement monitoring, distributed tracing, alerting, and full operational visibility. Solid experience with data and messaging technologies, including SQL/NoSQL databases, caching layers and message brokers such as PostgreSQL, Redis, Kafka, RabbitMQ, and Elasticsearch. 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 with Generative AI, including LLMs, RAG pipelines, embeddings, and agent frameworks, paired with familiarity across AI tooling ecosystems such as AWS Bedrock, LangChain, LangGraph, OpenAI APIs, and vector databases. Fluency in agile methodologies and delivery tooling such as Jira to manage team workflows and consistently ship high-quality software in fast-moving environments.
Advantages: Familiarity with Machine Learning or Data Science concepts, enabling better collaboration with AI-focused teams. Prior experience in AI/ML infrastructure, MLOps, or age
You’ll Do It Using: 7+ years of backend engineering experience, including 2+ years in a leadership role, with a hands-on background that informs architectural decisions, complex design reviews, and engineering mentorship that drives high-quality delivery. Deep expertise in microservices architecture, distributed systems, and cloud-native design on platforms such as AWS, GCP, or Azure using Kubernetes and Helm— with a proven ability to build scalable, resilient, and maintainable systems at scale. Strong command of observability practices, including hands-on experience with tools such as Prometheus, Grafana, or Splunk to implement monitoring, distributed tracing, alerting, and full operational visibility. Solid experience with data and messaging technologies, including SQL/NoSQL databases, caching layers and message brokers such as PostgreSQL, Redis, Kafka, RabbitMQ, and Elasticsearch. 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 with Generative AI, including LLMs, RAG pipelines, embeddings, and agent frameworks, paired with familiarity across AI tooling ecosystems such as AWS Bedrock, LangChain, LangGraph, OpenAI APIs, and vector databases. Fluency in agile methodologies and delivery tooling such as Jira to manage team workflows and consistently ship high-quality software in fast-moving environments.
Advantages: Familiarity with Machine Learning or Data Science concepts, enabling better collaboration with AI-focused teams. Prior experience in AI/ML infrastructure, MLOps, or age
This position is open to all candidates.
