Senior Applied AI Software Engineer

About the role

M3 MR, part of M3 Inc. (a Japanese global provider of healthcare market research, recruitment and data collection services), is hiring a Senior Applied AI Software Engineer. The role builds production systems combining software engineering, data science, large language models (LLMs), agentic AI and intelligent automation. It is a software engineering role first and an AI role second, working with Data Scientists, engineers, IT teams and business analysts. Initial focus areas include digital twin platforms, decision-support systems, AI-enabled research products, workflow automation, Copilot-style experiences and agentic AI.

Key responsibilities

• Design, build, test and maintain production software systems, services, APIs, integrations and automation workflows
• Build AI-enabled applications, copilots and workflow solutions using LLMs, agentic architectures and prompt and context engineering
• Implement RAG, tool-calling, memory and orchestration patterns, and evaluate and select AI models
• Optimise latency, quality, reliability and cost, and monitor and improve AI system performance
• Operationalise statistical, predictive and AI models, and contribute to digital twin and decision-support platforms
• Translate business requirements into scalable technical solutions and advise on feasibility and trade-offs

Required profile

• 5+ years of professional software engineering experience
• Experience delivering production applications and services, and building AI-enabled products or intelligent systems
• Experience across the full software development lifecycle
• Willingness to work in the US Eastern time zone

Required skills

• React, Node.js and TypeScript as the primary stack; Python for AI, automation and data workloads
• API development, SQL, data modelling and integration, automated testing, CI/CD
• Practical experience with LLMs, generative AI and prompt engineering
• RAG, agentic workflows, foundation model evaluation and optimisation
• Desirable: Azure, AWS or Google Cloud; vector databases and semantic search; MCP; MLOps; cloud-native deployment (Docker, Kubernetes); digital twin or decision science experience