M3USA

Senior Applied AI Software Engineer (US hours)

Manila, NCR, Philippines · Remote (restrictions not stated) · Full-time

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About the role

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EmployerM3USA
LocationManila, NCR, Philippines
Work styleRemote (restrictions not stated)
Employment typeFull-time
Employer posted20 days ago

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.

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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

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#LI-Remote

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