About The Team

The AI Center of Excellence (AI CoE) brings together AI Engineers and Data Scientists to research, prototype, and deliver production-grade AI systems. Our mission is to leverage cutting-edge Generative AI, agentic frameworks, and LLM-powered solutions to protect our customers attack surfaces.

We partner closely with Detection and Response teams, including our MDR service, to build intelligent AI systems that operate autonomously, reason across complex security data, and continuously evolve. We operate with a creative, iterative approach, building on 20+ years of threat analysis and a growing patent portfolio. We foster a collaborative environment focused on learning, mentorship, and practical impact.

If you're early in your AI engineering career and excited to build real-world agentic and GenAI systems in cybersecurity - this is your opportunity.

The Technologies We Use Include

Python - core language for AI/ML engineering
LLM/GenAI toolchains: LangChain, LangGraph, HuggingFace Transformers
Agentic AI concepts: tool-calling, ReAct patterns, single and multi-agent workflows
RAG pipelines: vector databases, embedding models, basic retrieval strategies
AWS cloud ecosystem: Bedrock, SageMaker, Lambda, S3
LLM observability & evaluation: Langfuse, Promptfoo (foundational exposure)
CI/CD for ML/LLM systems: GitHub Actions, Jenkins
Monitoring: CloudWatch, dashboards
Data science stack: scikit-learn, PyTorch, pandas, NumPy (supporting capabilities)

About The Role

Rapid7 is seeking an SE-II AI Engineer - Agentic & Generative AI to join our AI Center of Excellence as we expand our GenAI and agentic AI capabilities. This role is focused on building strong foundations in LLM engineering, contributing to agentic AI workflows, and growing into a well-rounded AI engineer in a production cybersecurity environment.

This Role Is Ideal For Someone Who Is

Developing hands-on skills in LLM orchestration, prompt engineering, and RAG pipelines
Gaining real-world experience with agentic AI patterns and AWS-based GenAI deployments
Building data science and ML fundamentals to complement AI-native work

In This Role, You Will

Agentic AI & LLM Systems

Contribute to building agentic AI workflows - tool-calling, basic agent loops, and LLM-driven automation under senior guidance
Assist in developing and maintaining RAG pipelines - document ingestion, chunking, embedding, and retrieval
Implement and iterate on prompt engineering - few-shot prompting, chain-of-thought, structured outputs
Work with LangChain / LangGraph for LLM orchestration and chaining tasks
Support LLM evaluation tasks - writing eval datasets, measuring output quality, running benchmarks
Contribute to observability and monitoring of LLM systems - latency, token usage, output quality dashboards
Deploy and test LLM-powered features on AWS Bedrock, Lambda, and SageMaker
Participate in prompt versioning and LLM CI/CD pipelines under guidance of senior engineers
Assist with guardrail implementation and output validation for production GenAI systems
Learn and apply agentic AI patterns - ReAct, tool-use APIs, and structured output parsing

ML & Data Science

Work on data acquisition, cleaning, enrichment, and transformation for AI/ML pipelines
Build and evaluate supervised ML models (classification, regression) for security use cases
Apply unsupervised ML techniques such as clustering and anomaly detection
Contribute to malware detection and user behavioral models under senior guidance
Support model deployment on AWS SageMaker and monitor performance using established dashboards

The Skills You'll Bring

Core - Agentic AI & LLM (Required)

2–5 years of experience in AI/ML engineering or software engineering with AI focus
Foundational hands-on experience with LangChain or similar LLM orchestration frameworks
Familiarity with prompt engineering concepts and techniques
Basic understanding of RAG pipelines - what they are, how retrieval works, and where they're applied
Awareness of agentic AI patterns - tool-calling, agent loops, ReAct
Exposure to LLM evaluation - understanding what good vs. bad LLM output looks like and how to measure it
Working knowledge of AWS Bedrock and/or SageMaker for AI/ML workloads
Strong Python skills and a learning-first mindset

Data Science & ML

Working proficiency with pandas, NumPy, scikit-learn
Solid understanding of supervised and unsupervised ML, feature engineering, and model evaluation metrics
Exposure to deep learning frameworks (PyTorch / TensorFlow)
Basic familiarity with model explainability (SHAP, LIME)

Advantageous

Exposure to cybersecurity, malware datasets, or threat detection domains
Hands-on experience with vector databases (FAISS, Pinecone, OpenSearch)
Familiarity with LLM observability tools — Langfuse, Promptfoo, or similar
Working knowledge of ML/LLM CI/CD pipelines
Basic understanding of multi-agent orchestration concepts

About Rapid7

At Rapid7, our vision is to create a secure digital world for our customers, our industry, and our communities. We do this by harnessing our collective expertise and passion to challenge what's possible and drive extraordinary impact. We're building a dynamic and collaborative workplace where new ideas are welcome.

Protecting 11,500+ customers against bad actors and threats means we're continuing to push the envelope just like we ve been doing for the past 20 years. If you re ready to solve some of the toughest challenges in cybersecurity, we're ready to help you take command of your career. Join us.

AI Engineer II