Senior Backend Developer

## About the Role Lyearn is the Unified AI-Native Employee Experience Platform. Where enterprises juggle 10–15 disconnected HR tools, Lyearn unifies them into one intelligent system. Powered by our proprietary MGSA engine (Mindset, Goals, Skills, Activities) and Productivity Models, Lyearn turns fragmented data into real-time intelligence that defines, measures, and evolves performance standards autonomously. Unlike legacy HR software with bolt-on AI, Lyearn is AI-native — AI is not an add-on, it is the platform. Every function - Learning, Performance, Engagement, Projects, Knowledge - is powered by AI that thinks, learns, and acts across the enterprise. ## Responsibilities ### Technical Leadership & Mentorship - Lead and mentor a team of backend engineers, providing technical guidance, code review oversight, and career development support to help team members grow their skills and advance their careers. - Drive architectural decisions and establish engineering best practices across the backend team, ensuring consistency, maintainability, and scalability in our codebase. - Collaborate closely with product, design, and DevOps teams to translate business requirements into technical solutions, balancing feature delivery with technical debt management. - Establish and maintain engineering standards including code quality guidelines, testing strategies, documentation practices, and deployment procedures. - Conduct technical interviews and participate in the hiring process to build a world-class engineering team. - Lead technical planning sessions and architecture reviews, helping the team make informed decisions about technology choices and system design. ### Individual Contribution & System Development - Design and build highly scalable, reliable, and secure backend systems using modern technologies including Node.js, TypeScript, Golang, Java, Spring Boot and Python. - Architect and implement distributed microservices with robust inter-service communication using gRPC, REST APIs, and GraphQL. - Build and maintain event-driven architectures leveraging Kafka and RabbitMQ to enable seamless, asynchronous communication across distributed systems. - Optimize database performance and design across multiple data stores including Couchbase and MongoDB for document storage, QDrant for vectors, Redis for caching and session management, and Elasticsearch for search and analytics. - Implement comprehensive monitoring, logging, and observability solutions to ensure system reliability and performance in production environments. - Design and maintain CI/CD pipelines using Jenkins and GitHub Workflows to enable automated testing, building, and deployment processes. - Deploy and manage containerized applications using Docker and Kubernetes across AWS and GCP cloud environments. ### AI/ML Integration & Advanced Features - Lead the integration of LLM APIs (OpenAI, Claude, etc.) into our learning management platform, creating intelligent features that enhance user experience and learning outcomes. - Design and implement sophisticated agentic workflows that automate complex business processes and provide intelligent recommendations to users. - Build and optimize vector store implementations using tools like Qdrant and FAISS to power semantic search, content recommendations, and personalized learning paths. - Develop and maintain RAG (Retrieval-Augmented Generation) pipelines that enhance our LLM outputs with contextual, real-time data from our learning platform. - Collaborate with AI/ML teams to integrate fine-tuned models and implement multi-agent systems that solve complex learning and performance management challenges. - Stay at the forefront of AI technology trends, experimenting with cutting-edge techniques and applying research insights to solve real-world business problems. ## Requirements ### Technical Expertise - You bring 5+ years of backend development experience with a proven track record of building and scaling distributed systems in production environments. - Expert-level proficiency in multiple programming languages including Node.js, TypeScript, Golang, Java, Spring Boot and Python, with the ability to choose the right tool for each specific challenge. - Deep experience with database technologies including MongoDB, CouchBase for document storage, Qdrant for vector retrieval Redis for caching and real-time features, and Elasticsearch for search and analytics. - Strong background in distributed systems architecture with hands-on experience implementing Kafka and RabbitMQ for event-driven communication patterns. - Extensive cloud platform experience across AWS and GCP, including infrastructure design, deployment strategies, and cost optimization. - Proficiency with containerization and orchestration using Docker and Kubernetes for scalable application deployment and management. - Experience building robust APIs using gRPC, REST, and GraphQL with a focus on security, performance, and maintainability. - Strong DevOps capabilities including CI/CD pipeline design with Jenkins and GitHub Workflows, infrastructure as code, and automated testing strategies. ### Leadership & Collaboration Skills - Proven experience mentoring and leading engineering teams, with the ability to guide technical decisions while fostering a collaborative and inclusive team environment. - Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders. - Experience in agile development environments with a track record of delivering high-quality software on schedule while maintaining technical excellence. - Strong problem-solving abilities and the capacity to break down complex challenges into manageable, actionable solutions. ### AI/ML & Modern Technology Experience - Hands-on experience integrating LLM APIs (OpenAI, Claude, etc.) into production applications, with understanding of prompt engineering and model optimization. - Practical experience with vector databases like Qdrant or FAISS for building semantic search and recommendation systems. - Experience designing and