1 day ago
Pune, IndiaStaff+
Responsibilities
- Lead the design and architecture of LLM and agentic AI systems across Detection and Response and Exposure and Remediation products.
- Define model strategies, including when to use frontier or open-source models and when to fine-tune, distill, or post-train them.
- Fine-tune, distill, and post-train open-source LLMs on security data through data curation, training, evaluation, and deployment.
- Develop context engineering, inference optimization, and evaluation practices while balancing quality, latency, and cost.
- Establish evaluation gates and benchmarks for shipped AI systems.
- Lead design reviews, resolve complex technical trade-offs, and contribute to technical roadmaps.
- Prototype and build critical model, training, and inference code.
- Mentor engineers and partner with Product and platform teams to translate customer needs into scalable AI solutions.
- Lead AI projects or programs, manage risks and dependencies, and drive complex technical outcomes across teams.
Requirements
- 8+ years of software or ML engineering experience, including 2+ years building production LLM or agentic AI systems.
- Track record of delivering scalable, production-grade AI systems used by multiple teams or products, with technical leadership and mentoring impact.
- Deep understanding of LLMs, including transformer architecture, attention, tokenization, positional encodings, training, and serving.
- Advanced PyTorch skills, including custom model code, training loops, mixed precision, and distributed training with technologies such as FSDP and DeepSpeed.
- Hands-on experience fine-tuning and post-training open-weight LLMs using full and parameter-efficient methods such as LoRA and QLoRA, along with distillation and preference- or reinforcement-learning-based methods such as DPO and GRPO.
- Experience with context engineering, retrieval, memory, long-running agent workflows, and evaluation strategies for LLM and agent systems.
- Strong knowledge of inference optimization, including prefill and decode, KV-cache management, continuous batching, speculative decoding, quantization, and serving at scale with vLLM, SGLang, or TensorRT-LLM.
- Strong predictive AI, classical ML, distributed systems, and Python fundamentals, with judgment to select appropriate approaches based on accuracy, latency, and cost.
- Ability to communicate complex technical concepts and trade-offs to technical and non-technical audiences.
- Ability to build cross-functional partnerships, manage dependencies, and align technical approaches with customer and business needs.
- A Master's degree in Computer Science, Machine Learning, AI, or a related field is preferred, or equivalent practical experience.
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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.