Responsibilities

• Design & implement backend services and APIs using Golang and Python (including micro‑services architecture) 
• Collaborate with ML engineers to deploy, monitor, and integrate machine‑learning models into production pipelines 
• Develop and maintain infrastructure for LLMs and AI agents, ensuring high performance, scalability, and reliability 
• Manage and optimize databases (MySQL, MongoDB) and work with vector databases or RAG systems where needed 
• Utilize AI‑assisted coding tools such as Codex and Claude Code to accelerate development 
• Build modern, responsive UI components using React and MUI 5, delivering seamless user experiences.
• Implement server‑side logic with Java, Spring, and Spring Boot, enabling robust, enterprise‑grade services.
• Deploy and orchestrate applications on Kubernetes clusters, leveraging AWS and Azure infrastructure services.

Requirements

• Bachelor’s degree (or higher) in Computer Science, Data Science, or a related field.
• 5+ years of professional experience in backend development (Golang/Python) and full‑stack development (React, Java/Spring).
• Strong understanding of AI Agentic workflows and experience integrating ML models into production 
• Proven experience with MySQL, MongoDB, and optionally vector databases or RAG systems 
• Hands‑on experience with micro‑services, cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes) 
• Familiarity with LLM and AI‑agent frameworks, and the ability to build supporting infrastructure 

• Experience with MUI 5 for building modern UI components.
• Proficiency in React (hooks, context, performance optimization).
• Strong Java development background, especially with Spring and Spring Boot.
• Knowledge of Kubernetes advanced concepts (Helm, operators, CI/CD pipelines).
• Hands‑on experience managing AWS services (ECS/EKS, Lambda, S3, RDS) and Azure services (AKS, Functions, Cosmos DB).
• Exposure to MLOps tools such as MLflow, Kubeflow, SageMaker.
• Experience with vector databases, retrieval‑augmented generation (RAG) systems, or embeddings.