Generative AI Engineer

About the Company

We are seeking a GenAI Engineer with 36 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 Role

This role requires strong engineering depth combined with applied AI expertise to deliver scalable, reliable, and efficient AI-powered applications.

Responsibilities

• Design 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.

Qualifications

• Experience: 36 years in AI/ML engineering, software engineering, or applied AI roles.

Required Skills

• Hands-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 Skills

• Experience 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.