Principal AI Engineer

mymoose; Ireland; On-site; EUR 150,000 - 210,000; Full time

Job summary

Rapid7 is expanding its AI Engineering organization to define and build the next generation of AI for security products. As a Principal AI Engineer, you will set the technical direction for LLMs and agentic systems, staying hands-on with core issues from transformer internals to deployment.

You will mentor senior engineers and influence product and platform investments across Detection, Response, and Remediation.

Qualifications

• 12+ years of software or ML engineering experience with leadership in AI systems.
• Proven ability to lead multi-team architectural decisions and mentor engineers.

Responsibilities

• Define architecture and technical roadmap for LLM and agentic AI systems across company security products.
• Lead model strategy: frontier vs open-source models, fine-tuning, distillation or post-training.
• Fine-tune, distill and post-train open-source LLMs on security data from data curation to deployment.
• Set engineering standards for context engineering and inference optimization.
• Optimize LLM inference at scale with techniques like speculative decoding and KV-cache management.
• Establish evals as release gates and lead design reviews.
• Stay hands-on: write critical model, training and inference code; mentor Staff and Senior engineers.

Skills

AI engineering

ML engineering

Python

PyTorch

Distributed systems

Inference optimization

LLMs

Mentoring

Education

PhD in Computer Science/ML or related field

Equivalent industry depth demonstrated

Tools

vLLM

TensorRT-LLM

DeepSpeed

FSDP

QLoRA/LoRA

Job description

Our Engineering organisation is scaling its AI Engineering capabilities under new leadership, creating an opportunity to build the next generation of AI at Rapid7. We're looking for Principal AI Engineers to join us and help shape what comes next by defining and influencing the technical direction of our LLM and agentic AI systems. As a Principal AI Engineer, you'll raise the bar for engineering standards, and build truly cutting edge greenfield solutions that deliver real impact for our Global customers. Through your technical leadership and mentoring, you'll help shape our product and platform investments and influence how we build and scale AI across the organisation.

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 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:

• Define the architecture and technical roadmap for LLM and agentic AI systems across Detection and Response and Exposure and Remediation
• Lead model strategy: when to use frontier or open-source models, and when to fine-tune, distill, or post-train
• Fine-tune, distill and post-train open-source LLMs on security data, from data curation through training, evaluation and deployment
• Set engineering standards for context engineering, inference optimization, and evaluation across teams
• Optimize LLM inference at scale with techniques such as speculative decoding, KV-cache management, continuous batching, quantization and custom kernels, balancing quality, latency and cost
• Establish evals as the release gate for every AI system we ship
• Lead design reviews and resolve the hardest technical trade-offs
• Stay hands-on: prototype and build critical model, training and inference code yourself
• Mentor Staff and Senior engineers and shape product and platform investment

The skills and qualities you'll bring include:

• 12+ 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 used across multiple teams or products with a strong history of organization-wide impact through technical leadership, mentoring, and shared platforms and standards
• LLMs from first principles: deep understanding of transformer architecture, attention, tokenization and positional encodings, and how models are trained and served, with the ability to implement core components from scratch
• PyTorch: expert-level, including custom model code, training loops, mixed precision and distributed training (e.g., FSDP, DeepSpeed)
• Fine-tuning and post-training: hands-on full and parameter-efficient fine-tuning (LoRA, QLoRA) of open-weight LLMs, distillation, and preference or RL-based methods (e.g., DPO, GRPO)
• Context engineering: retrieval, memory, and context management for long-running agent workflows
• Inference optimization: deep knowledge of prefill and decode, KV-cache management (e.g., PagedAttention, prefix caching), continuous batching, speculative decoding, FlashAttention and quantization, and serving at scale with vLLM, SGLang or TensorRT-LLM
• Evals: defining evaluation strategy and benchmarks for LLM and agent systems
• Strong predictive AI and classical ML fundamentals, and the judgment to choose between them and LLMs based on accuracy, latency and cost
• Strong distributed systems and Python fundamentals
• Takes ownership of significant technical outcomes across multipl