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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in AI technical vision and architecture, particularly in LLM and agentic systems, with a strong focus on fine-tuning, optimization, and evaluation strategies. Proven ability to lead complex initiatives, mentor engineering talent, and influence product and platform strategies in AI-powered cybersecurity.
Highest-signal resume keywords
14+ Years Software Engineering Experience2+ Years Building Production LLM SystemsPhD In Computer Science Or Related FieldAdvanced PyTorch SkillsDeep Knowledge Of Inference Optimization
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
LLM Fine-TuningDistillation MethodsContext EngineeringInference OptimizationTransformer ArchitectureTokenizationMixed Precision TrainingDistributed TrainingPython FundamentalsPredictive AI
Soft Skills
Mentoring EngineersInfluencing Technical DecisionsWorking Through AmbiguityAligning Cross-Functional TeamsDecision-Making
Tools & Technologies
PyTorchAWSCUDATritonVLLMSGLangTensorRT-LLM
Industry Keywords
AI SystemsAgentic SystemsCybersecurity ProductsModel StrategyEvaluation Strategies
Tech Stack
Tools & technologiesAWSCyber SecurityDistributed SystemsPythonPyTorch
About the role
Key responsibilities & impact- 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 use of frontier or open-source models, fine-tuning, distillation, post-training, and investment in new capabilities
- Lead complex, high-risk technical initiatives across multiple teams
- Lead fine-tuning, distillation, and post-training of open-source LLMs on security data
- Establish company-wide standards for context engineering, inference optimization, and evaluation
- Make evaluations the release gate for every AI system Rapid7 ships
- Lead optimization of LLM inference at scale while balancing quality, latency, and cost
- Lead design reviews and resolve complex technical trade-offs
- Prototype and build critical model, training, and inference code
- Mentor and develop Principal, Staff, and Senior engineers
- Build AI engineering talent depth across Rapid7
- Represent Rapid7’s AI engineering to executives, customers, and the external community
- Influence product, platform, and business strategy
- Shape strategic product and platform investments for AI-powered cybersecurity products
Requirements
What you’ll need- 14+ years of software or ML engineering experience
- 2+ years building production LLM or agentic AI systems
- PhD in Computer Science, Machine Learning, AI, or a related field preferred, or equivalent depth of expertise demonstrated through industry experience and technical contributions
- Track record of building scalable, production-grade AI systems and shared platforms adopted across multiple products
- Leading-edge expertise in LLMs, including transformer architecture, attention, tokenization, and positional encodings
- Advanced PyTorch skills, including custom model code, mixed precision, and distributed training such as FSDP and DeepSpeed
- Hands-on fine-tuning and post-training of open-weight LLMs, including full and parameter-efficient methods such as LoRA and QLoRA
- Experience with distillation and preference or RL-based methods such as DPO and GRPO
- Deep experience with context engineering, retrieval, memory, and context management for long-running agent workflows
- Experience defining evaluation strategies 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
- Experience serving models at scale with vLLM, SGLang, or TensorRT-LLM
- Ideally experience with AWS and GPU kernel work using CUDA or Triton
- Strong predictive AI, classical ML, distributed systems, and Python fundamentals
- Ability to make transparent decisions between predictive AI, classical ML, and LLM approaches based on accuracy, latency, and cost
- Recognition as an AI expert through publications, patents, open-source contributions, or industry engagement
- Ability to influence technical and product decisions across the organisation
- Ability to align Engineering, Product, and other functions on technical direction
- Ability to work through ambiguity and evolving AI technology and threat landscapes
- Alignment with Rapid7’s core values
Benefits
Comp & perks- Flexible/remote work options indicated by Belfast and UK Remote locations
- Opportunities for technical leadership and mentoring
- Opportunity to influence strategic product, platform, and business strategy
- Opportunity to work on greenfield AI innovation
- Professional growth through shaping AI engineering capabilities
- Collaborative, multi-dimensional team environment