{"id":3703665,"url":"https://alion.io/job/rapid7-principal-ai-engineer","title":"Principal AI Engineer","company":{"id":12657,"name":"Rapid7","domain":"rapid7.com","url":"https://alion.io/company/rapid7","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"DeepSpeed","optional":false},{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"GRPO","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"SGLang","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Tokenization","optional":false},{"name":"vLLM","optional":false},{"name":"AWS","optional":true},{"name":"CUDA","optional":true},{"name":"PEFT","optional":true},{"name":"Pre-training","optional":true},{"name":"TensorRT","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-11-03T17:23:35Z","employer_posted_date":null,"last_verified_at":"2026-11-03T17:23:35Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Our Engineering organisation is scaling its AI Engineering capabilities under new leadership, creating an opportunity to build the next generation of AI at Rapid7. We're looking for Principal AI Engineers to join us and help shape what comes next by defining and influencing the technical direction of our LLM and agentic AI systems. As a Principal AI Engineer, you'll raise the bar for engineering standards, and build truly cutting edge greenfield solutions that deliver real impact for our Global customers. Through your technical leadership and mentoring, you'll help shape our product and platform investments and influence how we build and scale AI across the organisation.\n\nAbout The Team\n\nAI is reshaping cybersecurity on both sides. Attackers can use AI to strike at machine speed, and only AI-powered defense can keep up. At the same time, organizations can only adopt AI with confidence if it is secure. Rapid7 sits at the intersection of security and AI, and we are building industry-defining products for this new era.\n\nRapid7's AI Engineering team builds the AI that powers our security platform. We are AI engineers and applied scientists who take models from research to production, combining open-source and frontier LLMs, AI agents, and fast predictive models with more than 20 years of Rapid7 security expertise. Our work drives core use cases across Detection and Response, such as alert triage, threat investigation, and automated response, and Exposure and Remediation, such as vulnerability prioritization, attack surface risk assessment, and guided remediation. Together, these help customers stop threats faster and fix what matters most.\n\nAbout The Role\n\nAs a Principal AI Engineer, you will set the technical direction for how Rapid7 builds, tunes and serves LLMs and agentic systems across its security products. You understand LLMs from the ground up, from transformer internals and training to fine-tuning and inference optimization, and you stay hands-on with the hardest problems. You know where LLMs shine and where they fall short, and you treat them as one tool in the AI toolbox, alongside predictive AI and classical ML, choosing the right approach for each problem. Specifically your focus will be to:\n\nDefine the architecture and technical roadmap for LLM and agentic AI systems across Detection and Response and Exposure and Remediation\nLead model strategy: when to use frontier or open-source models, and when to fine-tune, distill, or post-train\n Fine-tune, distill and post-train open-source LLMs on security data, from data curation through training, evaluation and deployment\nSet engineering standards for context engineering, inference optimization, and evaluation across teams\nOptimize LLM inference at scale with techniques such as speculative decoding, KV-cache management, continuous batching, quantization and custom kernels, balancing quality, latency and cost\n Establish evals as the release gate for every AI system we ship\nLead design reviews and resolve the hardest technical trade-offs\nStay hands-on: prototype and build critical model, training and inference code yourself\nMentor Staff and Senior engineers and shape product and platform investment\n\nThe Skills And Qualities You'll Bring Include\n\n12+ years of software or ML engineering experience, including 2+ years building production LLM or agentic AI systems. 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\nA track record of building scalable, production-grade AI systems used across multiple teams or products with a strong history of organization-wide impact through technical leadership, mentoring, and shared platforms and standards\n LLMs from first principles: deep understanding of transformer architecture, attention, tokenization and positional encodings, and how models are trained and served, with the ability to implement core components from scratch\nPyTorch: expert-level, including custom model code, training loops, mixed precision and distributed training (e.g., FSDP, DeepSpeed)\nFine-tuning and post-training: hands-on full and parameter-efficient fine-tuning (LoRA, QLoRA) of open-weight LLMs, distillation, and preference or RL-based methods (e.g., DPO, GRPO)\n Context engineering: retrieval, memory, and context management for long-running agent workflows\nInference optimization: deep knowledge of prefill and decode, KV-cache management (e.g., PagedAttention, prefix caching), continuous batching, speculative decoding, FlashAttention and quantization, and serving at scale with vLLM, SGLang or TensorRT-LLM\n Evals: defining evaluation strategy and benchmarks for LLM and agent systems\nStrong predictive AI and classical ML fundamentals, and the judgment to choose between them and LLMs based on accuracy, latency and cost\nStrong distributed systems and Python fundamentals\nTakes ownership of significant technical outcomes across multiple teams, proactively identifying and addressing risks, driving complex challenges to resolution and ensuring solutions deliver against agreed technical and customer objectives\nStrong communicator with the ability to explain technical trade-offs to engineering, product, and leadership audiences\nBuilds strong cross functional partnerships across Engineering, Product, and other teams to align on technical direction and deliver shared outcomes\nConnects customer needs and business priorities to technical decisions, evaluating the value, cost and trade-offs of AI solutions to ensure investments deliver measurable customer and business outcomes\nEmbody our core values to foster a culture of excellence that drives meaningful impact and collective success\n\nPreferred Skills And Experience\n\nExperience pre-training or training LLMs from scratch\nExperience writing GPU kernels (CUDA or Triton) and profiling GPU workloads\nSecurity domain experience, such as detection engineering, SOC/MDR, or vulnerability management\nExperience running AI workloads on AWS at scale\nPublications, patents, or open-source contributions in AI\n\nWe know that the best ideas and solutions come from multi-dimensional teams. That's because these teams reflect a variety of backgrounds and professional experiences. If you are excited about this role and feel your experience can make an impact, please don't be shy - apply today.\n\nAbout Rapid7\n\nAt 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.\n\nProtecting 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. 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