Senior Applied AI Software Engineer (US hours)

Role Purpose Building production systems powered by software engineering, data science and modern AI. We are seeking a hands-on Senior Applied AI Software Engineer to design, build and deploy production systems that combine software engineering, data science, large language models (LLMs), agentic AI and intelligent automation. This is a software engineering role first and an AI role second. We are looking for someone who enjoys building real systems, shipping code and solving business problems, while applying modern AI technologies where they create meaningful value. You will work alongside Data Scientists, Engineers, IT teams and Business Analysts to transform ideas, models and business requirements into robust, scalable solutions. This includes operationalising statistical and decision-science models, building AI-powered products and workflows, selecting and integrating foundation models, optimising cost and performance, and developing agent-based systems that automate and enhance complex business processes. Initial focus areas include digital twin platforms, operational decision-support systems, AI-enabled research products, intelligent workflow automation, Copilot-style experiences and agentic AI solutions embedded across the organisation. Success in the role will be measured by the delivery of reliable, maintainable production systems that create measurable business value, rather than experimentation alone. Key Responsibilities Software Engineering & Product Delivery Design, build, test and maintain production software systems. Develop scalable services, APIs, integrations and automation workflows. Contribute to architecture, engineering standards and best practices. Support deployment, monitoring and continuous improvement of production solutions. Applied AI & Intelligent Systems Design and deploy AI-enabled solutions using LLMs, agentic architectures and automation. Build production AI applications, copilots and workflow solutions. Develop prompt and context engineering frameworks. Implement RAG, tool-calling, memory and orchestration patterns. Evaluate and select appropriate AI models and technologies. Optimise latency, quality, reliability and cost. Monitor, test and continuously improve AI system performance. Data & Decision Science Enablement Operationalise statistical, predictive and AI models. Build validation, monitoring and feedback mechanisms. Contribute to digital twin and decision-support platforms. Business Collaboration & Technical Leadership Translate business requirements into scalable technical solutions. Shape ideas into practical products and capabilities. Advise on feasibility, trade-offs and implementation approaches. Help define how AI is applied across the organisation through reusable platforms, patterns and capabilities. Essential Experience 5+ years of professional software engineering experience. Experience delivering production applications and services. Experience building AI-enabled products or intelligent systems. Experience across the full software development lifecycle. Technical Skills Strong React, Node.js and TypeScript as the primary development stack Python for AI, automation and data-related workloads where appropriate. API development, systems integration and scalable architecture. SQL, data modelling and data integration. Automated testing, CI/CD and modern engineering practices. Practical experience with LLMs, generative AI and prompt engineering. Experience with RAG, agentic workflows and AI orchestration patterns. Foundation model evaluation, selection and optimisation. Monitoring, testing and support of AI systems in production. Desirable Experience Azure, AWS or Google Cloud. Vector databases and semantic search. MCP: connecting AI to enterprise tools and data. MLOps and model lifecycle management. AI evaluation/observability: measuring and monitoring model performance in production. Cloud-native deployment: best practices for building scalable, reliable AI applications designed to run in the Cloud (Docker, Kubernetes etc). Digital twin, optimisation or decision science experience. Healthcare, data services or market research experience. Working hours: Must be willing to work in US Eastern time zone Please submit your CV in English #LI-LC1 #LI-Remote About M3: A Japanese global leader in the provision of ground-breaking and innovative technological and research solutions to the healthcare industry. The M3 Group operates in the US, Asia, and Europe with over 5.8 million physician members globally via its physician websites which include , , , , , and . M3 Inc. is a publicly traded company on the Tokyo Stock Exchange (jp:2413, NIKKEI 225) with subsidiaries in major markets including the US, UK, Japan, South Korea, and China, and in 2020 was ranked in Forbes’ Global 2000 list. The M3 Group provides services to healthcare and the life science industry. In addition to market research, these services include medical education, ethical drug promotion, clinical development, job recruitment, and clinic appointment services. M3 has offices in Japan, UK, France, Germany, Brazil, Sweden, China, USA, and South Korea, as well as India. Due to continued growth, M3 MR is seeking a Senior Applied AI Software Engineer Business Unit Mission: M3 MR, part of M3 Inc., provides the most comprehensive and highest-quality healthcare market research recruitment, data collection, and insight support services globally. With proprietary access to healthcare professionals and patient communities across more than 70 countries, M3 MR partners with pharmaceutical, biotech, medical device, and market research agencies to deliver trusted, compliant, and decision-ready insights at speed and scale. M3 MR holds ISO 20252, ISO 27001, and ISO 27701 certifications, reflecting the group’s commitment to data quality, respondent integrity, information security, and operational excellence across quantitative and qualitative methodologies. The group combines deep healthcare expertise, advanced technology platforms, and global operational reach to support clients across the full research lifecycle.