AI Engineer

ELSA, Corp is a global leader in AI-powered English communication training, dedicated to transforming how people learn and speak English with confidence. The AI Engineer role involves developing and deploying LLM-powered capabilities to enhance conversational tutoring, requiring collaboration with researchers and engineers to implement AI in scalable applications.

Responsibilities:

• Design, build, and deploy production-grade AI agents for conversational and task-oriented experiences
• Architect scalable agentic systems (memory, tool orchestration, multi-step workflows), ensuring reliability and impact
• Implement robust evaluation, observability, and feedback loops to drive continuous improvement in performance and cost efficiency
• Develop secure, interoperable tool integrations (APIs, external systems, structured retrieval) adhering to modern standards
• Collaborate with research, product, and engineering to translate AI capabilities into scalable, user-facing applications
• Integrate speech technologies (ASR/TTS) into conversational AI systems where applicable

Requirements:

• Strong experience building and deploying AI systems powered by LLMs, with a focus on real-world reliability and user impact
• Solid understanding of agentic architectures, including tool orchestration, memory design, multi-step reasoning, and structured workflows
• Deep experience with multi-agentic memory systems, including episodic, semantic, and procedural memory design, shared memory across agents, memory consolidation strategies, and context window management in long-horizon tasks
• Experience integrating AI systems with external APIs, structured data sources, and retrieval systems in production environments
• Hands-on experience with MCP (Model Context Protocol) integration — including building, deploying, and managing MCP servers, tool/resource/prompt exposure patterns, and secure client-server communication in agentic pipelines
• Hands-on experience with evaluation, monitoring, and performance optimization of AI applications (latency, cost, robustness, safety)
• Strong software engineering fundamentals, including distributed systems, APIs, containerization, and cloud-native deployment
• Practical knowledge of prompt design, model adaptation (fine-tuning or parameter-efficient approaches), and controlled generation techniques
• Understanding of security considerations in AI systems, including prompt injection risks, tool permission scoping, and data handling practices
• Experience designing and orchestrating multi-agent systems with role specialization, inter-agent communication protocols, and coordination patterns (supervisor, peer-to-peer, hierarchical)
• Experience working cross-functionally with research, product, and engineering teams in fast-moving environments
• Experience with multi-agent systems, planner–executor architectures, or structured reasoning frameworks
• Familiarity with interoperability standards and tool communication protocols for agentic ecosystems
• Experience designing long-context or memory-augmented AI systems (episodic and semantic memory strategies)
• Knowledge of advanced model optimization techniques, including parameter-efficient fine-tuning, distillation, or model compression
• Experience integrating AI systems with speech technologies (ASR and TTS) for real-time conversational applications
• Background in NLP, conversational AI, or education-focused AI products