Senior AI Engineer

As a Senior AI Engineer , you will design, deploy, and optimize LLM-based services in a self-hosted cloud setup. You will build a centralized LLM Gateway, implement agent-to-agent communication protocols, and develop memory systems for intelligent behavior. You will also manage MLOps pipelines and ensure system scalability and performance.

Key responsibilities

• Design, deploy, and optimize LLM-based services, with models running in a self-hosted setup on cloud infrastructure.
• Build and maintain a centralized LLM Gateway to manage multi-model access and routing
• Implement and evolve Agent-to-Agent (A2A) communication and the Multi-Component Protocol (MCP) for agent collaboration.
• Design and integrate a powerful Agent Memory System, including a dynamic knowledge base and contextual memory to empower intelligent behavior.
• Apply model optimization techniques to improve inference efficiency and cost-effectiveness.
• Develop and operate MLOps pipelines for model lifecycle management.
• Ensure system scalability, reliability, and performance across diverse workloads.
• Collaborate across teams to bring intelligent agents into real-world applications.

Qualifications

• Education: Bachelor’s degree/ Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related field.
• Minimum 5 years of experience in AI/ML/DL Engineer or similar roles.
• Strong programming skills in Python and experience with containerized and cloud-native environment.
• Solid understanding of AI/ML/DL model deployment, including serving, optimization, and context-aware design.
• Experience in building systems with agent orchestration, memory management, and structured communication protocols.
• Familiarity with retrieval-augmented generation, semantic memory, and message-driven workflows.
• Hands-on experience working with GPU (NVIDIA) or TPU environments, including model quantization and other performance optimization techniques.
• Practical experience with MLOps pipelines for training, versioning, and deploying machine learning models.
• Bonus: experience with agent-based architecture, LLM streaming patterns, Golang, and a strong foundation in mathematics, statistics, or linear algebra.