1 day ago
Pune, IndiaStaff+
Responsibilities
- Define architecture and technical roadmaps for LLM and agentic AI systems across Detection and Response and Exposure and Remediation.
- Lead model strategy, including selecting frontier or open-source models and determining when to fine-tune, distill, or post-train them.
- Fine-tune, distill, and post-train open-source LLMs on security data from curation through training, evaluation, and deployment.
- Set engineering standards for context engineering, inference optimization, and AI evaluation across teams.
- Optimize LLM inference at scale using techniques including speculative decoding, KV-cache management, continuous batching, quantization, and custom kernels.
- Establish evaluations as release gates for AI systems and lead design reviews and complex technical decisions.
- Prototype and build critical model, training, and inference code hands-on.
- Mentor Staff and Senior engineers and shape product and platform investments.
- Connect customer and business priorities to AI technical decisions and build cross-functional alignment across Engineering, Product, and other teams.
Requirements
- 12+ years of software or ML engineering experience, including 2+ years building production LLM or agentic AI systems.
- Track record of building scalable, production-grade AI systems used across multiple teams or products and delivering organization-wide impact through technical leadership, mentoring, shared platforms, and standards.
- Deep understanding of transformer architecture, attention, tokenization, positional encodings, and model training and serving, with the ability to implement core components from scratch.
- Expert-level PyTorch experience, including custom model code, training loops, mixed precision, and distributed training such as FSDP and DeepSpeed.
- Hands-on experience with full and parameter-efficient fine-tuning, including LoRA and QLoRA, distillation, and preference- or reinforcement-learning-based methods such as DPO and GRPO.
- Experience with retrieval, memory, and context management for long-running agent workflows.
- Deep knowledge of inference optimization, including prefill and decode, KV-cache management, PagedAttention, prefix caching, continuous batching, speculative decoding, FlashAttention, quantization, and serving at scale with vLLM, SGLang, or TensorRT-LLM.
- Strong predictive AI, classical ML, distributed systems, and Python fundamentals.
- Ability to own significant technical outcomes across multiple teams, resolve complex challenges, communicate technical trade-offs, and build cross-functional partnerships.
- Preferred experience pre-training or training LLMs from scratch; writing GPU kernels with CUDA or Triton; profiling GPU workloads; working in security domains such as detection engineering, SOC/MDR, or vulnerability management; running AI workloads on AWS at scale; and contributing publications, patents, or open-source work in AI.
- 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.
Categories
About Rapid7
Rapid7 builds a cloud security operations platform and services for enterprises, combining vulnerability management, detection and response (SIEM/XDR and MDR), application security testing, and security automation. Its Insight platform products include InsightVM, InsightIDR, InsightAppSec, and InsightConnect, sold as subscriptions with optional managed services. Founded in 2000 and headquartered in Boston, the NASDAQ-listed company (RPD) serves more than 11,500 customers worldwide.