Innovation Engineer (AI/ML)

Location: Hybrid in Galway, Ireland; work from an HPE office an average of 2 days per week

Company

hirify.global is a global edge-to-cloud company developing solutions that connect, protect, analyze, and manage data and applications from edge to cloud.

What you will do

• Research current AI and machine learning technologies, frameworks, algorithms, and emerging applications.
• Design and implement AI/ML solutions for the Hybrid Cloud portfolio in collaboration with cross-functional teams.
• Build and optimize high-throughput, low-latency LLM inference pipelines using serving engines such as vLLM or SGLang.
• Design autonomous agentic workflows and multi-agent systems with Agent-to-Agent communication, reasoning loops, tool calling, and orchestration frameworks such as LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Integrate, deploy, monitor, and fine-tune models within existing software systems and infrastructure, using performance metrics to improve algorithms and reliability.
• Partner with engineering leadership and senior stakeholders on standards, implementation decisions, strategic presentations, reports, and recommendations.

Requirements

• Bachelor’s or master’s degree in computer science, engineering, data science, machine learning, artificial intelligence, or a closely related quantitative discipline.
• Typically 4–7 years of experience.
• Deep understanding of machine learning algorithms, including regression, decision trees, support vector machines, random forests, neural networks, deep learning, and reinforcement learning.
• Strong programming skills in Python, Go, or Java, including production-level development, software engineering best practices, version control such as GitHub, and software development methodologies.
• Strong understanding of GitHub Copilot, Cursor, N8N, vibe coding, Windsurf, and similar technologies.
• Experience with cloud infrastructure such as AWS or Azure; knowledge of open source technologies and Linux.
• Understanding of DevOps and SRE practices.
• Understanding of convolutional and recurrent neural networks, transformers, transfer learning, generative models, and deep learning optimization algorithms.
• Hands-on experience scaling and optimizing LLM inference, conducting AI/ML research, and applying state-of-the-art techniques to complex problems.
• Excellent communication, problem-solving, and critical-thinking skills for collaboration with cross-functional teams and stakeholders; experience with TensorFlow, PyTorch, and scikit-learn is highly desirable.

Culture & Benefits

• Hybrid work with flexibility to manage work and personal needs.
• Health and wellbeing benefits for employees and their loved ones.
• Personal and professional development programs supporting progression into expertise or other divisions.
• Inclusive workplace committed to accessibility and equal opportunity.