Senior Principal AI Engineer

mymoose; Ireland; On-site; EUR 180,000 - 230,000; Full time

Job summary

Rapid7 is scaling its AI Engineering capabilities to shape the next generation of AI across security products. As a Senior Principal AI Engineer, you will define technical direction for LLMs and agentic systems, balance frontier and open-source approaches, and mentor senior engineers while driving cross‑team architectural decisions.

You will lead high‑impact initiatives, prototype critical model and inference code, and partner with executives to influence product strategy.

Qualifications

• Over 14 years in software or ML engineering, with production LLM/agentic AI systems
• PhD in Computer Science, Machine Learning or related field preferred
• Proven track record building scalable AI systems adopted across products
• Strong PyTorch skills with distributed training experience

Responsibilities

• Set the AI technical vision, architecture and multi-year roadmap for LLM/agentic systems
• Define company-wide model strategy for frontier/open-source vs tuned models
• Lead high-risk initiatives including fine-tuning, distillation and post-training of LLMs
• Establish standards for context engineering, inference optimization and eval strategies
• Lead scale-up of LLM inference balancing quality, latency and cost
• Mentor senior engineers and represent AI engineering to executives and customers

Skills

LLM systems

Python

PyTorch

Distributed systems

Mentoring

Education

PhD in CS/ML

Tools

vLLM/TensorRT-LLM

DeepSpeed

AWS

Job description

Our Engineering organisation is scaling its AI Engineering capabilities under new leadership, creating an opportunity to shape the next generation of AI at Rapid7. We're looking for Senior Principal AI Engineers to define and influence the technical direction of our LLM and agentic AI systems across the organisation. You'll tackle our most complex technical challenges, establish architectural principles and engineering standards, and drive greenfield innovation that delivers meaningful impact for our global customers. Through technical leadership and mentoring, you'll shape strategic product and platform investments and influence how we build and scale AI across Rapid7.

About the Team

AI is reshaping cybersecurity on both sides. Attackers can use AI to strike at machine speed, and only AI-powered defense can keep up. At the same time, organizations can only adopt AI with confidence if it is secure. Rapid7 sits at the intersection of security and AI, and we are building industry-defining products for this new era.

Rapid7's AI Engineering team builds the AI that powers our security platform. We are AI engineers and applied scientists who take models from research to production, combining open-source and frontier LLMs, AI agents, and fast predictive models with more than 20 years of Rapid7 security expertise. Our work drives core use cases across Detection and Response, such as alert triage, threat investigation, and automated response, and Exposure and Remediation, such as vulnerability prioritization, attack surface risk assessment, and guided remediation. Together, these help customers stop threats faster and fix what matters most.

About the Role

As a Senior Principal AI Engineer, you will set the technical direction for how Rapid7 builds, tunes and serves LLMs and agentic systems across its security products. You understand LLMs from the ground up, from transformer internals and training to fine‑tuning and inference optimization, and you stay hands‑on with the hardest problems. You know where LLMs shine and where they fall short, and you treat them as one tool in the AI toolbox, alongside predictive AI and classical ML, choosing the right approach for each problem. Specifically your focus will be to:

• Set the AI technical vision, architecture and multi-year roadmap for LLM and agentic systems across Detection and Response and Exposure and Remediation
• Define company-wide model strategy, including when to use frontier or open‑source models, when to fine‑tune, distill or post‑train, and where to invest in new capabilities
• Lead the most complex and highest‑risk technical initiatives across multiple teams, including fine‑tuning, distillation and post‑training of open‑source LLMs on security data
• Establish company-wide standards for context engineering, inference optimization and evaluation, with evals as the release gate for every AI system we ship
• Lead optimization of LLM inference at scale, balancing quality, latency and cost
• Lead design reviews, resolve the most complex technical trade‑offs, and stay hands‑on by prototyping and building critical model, training and inference code
• Mentor and develop Principal, Staff and Senior engineers, building the depth of AI engineering talent across Rapid7
• Represent Rapid7's AI engineering to executives, customers and the external community, influencing product, platform and business strategy

The skills and qualities you'll bring include:

• 14+ years of software or ML engineering experience, including 2+ years building production LLM or agentic AI systems. A PhD in Computer Science, Machine Learning, AI or a related field is preferred, or equivalent depth of expertise demonstrated through industry experience and technical contributions
• A track record of building scalable, production‑grade AI systems and shared platforms adopted across multiple products, with company‑wide impact through technical leadership, mentoring and standards
• Leading‑edge expertise in LLMs, including transformer architecture, attention, tokenization and positional encodings, and how models are trained and served, with advanced PyTorch skills spanning custom model code, mixed precision and distributed training (e.g., FSDP, DeepSpeed)
• Hands‑on fine‑tuning and post‑training of open‑weight LLMs, including full and parameter‑efficient methods (LoRA, QLoRA), distillation, and preference or RL‑based methods (e.g., DPO, GRPO), ideally including experience training LLMs from scratch
• Deep experience with context engineering (retrieval, memory, and context management for long‑running agent workflows) and with defining evaluation strategy and benchmarks for LLM and agent systems
• Deep knowledge of inference optimization, including prefill and decode, KV‑cache management, continuous batching, speculative decoding and quantization, and serving at scale with vLLM, SGLang or TensorRT‑LLM, ideally on AWS and including GPU kernel work (CUDA or Triton)
• Strong predictive AI, classical ML, distributed systems and Python