Overview

Our Engineering organisation is scaling its AI Engineering capabilities under new leadership, creating an opportunity to shape the next generation of AI at Rapid7.

Responsibilities

• Lead the design and architecture of LLM and agentic AI systems within Detection and Response and Exposure and Remediation, contributing to the technical roadmap for your area
• Recommend model strategy for your projects, including 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
• Apply and improve engineering practices for context engineering, inference optimization and evaluation, optimizing LLM inference to balance quality, latency and cost
• Establish evals as the release gate for the AI systems you ship
• Lead design reviews for your projects and work through complex technical trade-offs
• Stay hands-on by prototyping and building critical model, training and inference code
• Mentor engineers on your team and partner with Product and platform teams to align technical solutions with customer needs
• 8+ years of software or ML engineering experience including 2+ years building production LLM or agentic AI systems. A Master’s degree in Computer Science, Machine Learning, AI or a related field is preferred, or equivalent practical experience
• A track record of leading the delivery of scalable, production-grade AI systems used by multiple teams or products, with impact across the organisation through technical leadership and mentoring
• A deep understanding of LLMs, including transformer architecture, attention, tokenization and positional encodings, and how models are trained and served
• Advanced PyTorch skills, including custom model code, training loops, 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)
• Experience with context engineering (retrieval, memory, and context management for long-running agent workflows) and defining evaluation strategy and benchmarks for LLM and agent systems
• Strong 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
• Strong predictive AI, classical ML, distributed systems and Python fundamentals, with the judgment to make efficient, well-reasoned decisions between these approaches and LLMs based on accuracy, latency and cost
• Lead AI projects or programs of moderate scope and risk, contributing to the execution strategy, breaking larger goals into milestones, and holding yourself and others to clear expectations and commitments
• Takes ownership of complex technical outcomes across teams, proactively managing risks and dependencies, following through on commitments and driving issues to resolution.
• A strong communicator who can explain complex technical concepts and trade-offs to technical and non-technical audiences, building alignment around engineering decisions
• Builds strong cross-functional partnerships across Engineering, Product and other teams to resolve dependencies, align technical approaches and deliver shared outcomes
• Translates customer needs into scalable AI solutions, balancing customer value, performance and the cost of developing and running AI systems to deliver commercially viable outcomes
• Embody our core values to foster a culture of excellence that drives meaningful impact and collective success.