Senior Applied AI Software Engineer

M3USA

19h•Remote

About The Position

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. Due to continued growth, M3 MR is seeking a Senior Applied AI Software Engineer. The role's purpose is to build 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 operationalizing statistical and decision-science models, building AI-powered products and workflows, selecting and integrating foundation models, optimizing 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 organization. Success in the role will be measured by the delivery of reliable, maintainable production systems that create measurable business value, rather than experimentation alone.

Requirements

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.
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 optimization.
Monitoring, testing, and support of AI systems in production.

Nice To Haves

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, optimization, or decision science experience.
Healthcare, data services, or market research experience.

Responsibilities

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.
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.
Optimize latency, quality, reliability, and cost.
Monitor, test, and continuously improve AI system performance.
Operationalize statistical, predictive, and AI models.
Build validation, monitoring, and feedback mechanisms.
Contribute to digital twin and decision-support platforms.
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 organization through reusable platforms, patterns, and capabilities.