About the CompanyWe are seeking a GenAI Engineer with 3–6 years of experience to design and build production-grade AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based architectures. This role requires strong engineering depth combined with applied AI expertise to deliver scalable, reliable, and efficient AI-powered applications.About the RoleThis role requires strong engineering depth combined with applied AI expertise to deliver scalable, reliable, and efficient AI-powered applications.ResponsibilitiesDesign and build production-grade AI features leveraging LLMs, RAG pipelines, and agent-based systems.Implement end-to-end RAG pipelines including retrieval strategies, embeddings, vector search optimization, context orchestration, guardrails, and fallback mechanisms.Orchestrate agent workflows using frameworks such as LangGraph, LangChain, or similar, ensuring clear state management and safe multi-step reasoning.Develop and deploy cloud-native microservices and APIs (REST/gRPC) to serve AI applications at scale.Build and optimize event-driven architectures for real-time AI system interactions.Work with graph databases (e.g., Neo4j, Amazon Neptune) to model and query complex relationships.Fine-tune models and optimize performance for domain-specific use cases.Ensure production reliability including latency optimization, throughput scaling, token efficiency, and cost control.Implement observability, monitoring, and logging for AI systems, including prompt/model versioning and rollback strategies.Collaborate with cross-functional teams including data scientists, engineers, and product stakeholders to deliver impactful AI solutions. QualificationsExperience: 3–6 years in AI/ML engineering, software engineering, or applied AI roles.Required SkillsHands-on experience building production-grade applications using LLMs, RAG, and agent-based systems.Strong programming skills in Python, with exposure to Node.js, Java, or Go.Experience designing cloud-native architectures including microservices, REST/gRPC APIs, and event-driven systems.Solid understanding of embeddings, vector databases, and retrieval strategies.Experience with agent orchestration frameworks (e.g., LangChain, LangGraph) or similar.Exposure to model fine-tuning and performance optimization techniques.Experience working with graph databases such as Neo4j or Amazon Neptune.Strong understanding of system performance, scalability, and cost optimization in AI systems.Experience implementing observability, monitoring, and debugging for production systems.Strong problem-solving skills and ability to work in a fast-paced, collaborative environment. Preferred SkillsExperience with cloud platforms (AWS, GCP, or Azure) for deploying AI systems.Familiarity with containerization and orchestration (Docker, Kubernetes).Exposure to prompt engineering techniques and evaluation frameworks.Experience working in consulting or client-facing environments.