Principal AI – Large Language Models Expert

We are seeking a Principal AI – Large Language Model (LLM) Expert to lead the design, training, optimization, and deployment of language models across our privacy-first security platform. This role combines deep research expertise with hands-on engineering, spanning foundation model development, task-specific fine-tuning, and efficient Small Language Model (SLM) deployment in privacy-sensitive, resource-constrained environments.You will bridge cutting-edge LLM research with privacy-preserving constraints and real-world security requirements, owning architectural decisions, data strategy, evaluation rigor, and performance optimization from lab to production. As a founding technical leader, you'll shape how we embed LLMs into detection, classification, and response workflows without compromising user privacy or security posture.Key ResponsibilitiesLLM Architecture & Research: Lead research and implementation of Transformer-based architectures optimized for security classification tasks. Evaluate architectural designs for performance, efficiency, privacy-preservation, and robustness against adversarial inputs. Define threat-detection-specific requirements (evasion techniques, multilingual phishing, sophisticated BEC patterns).Pre-training & Foundation Models: Optimize training strategies, distributed training across GPUs/TPUs, and compute efficiency. Define evaluation criteria for foundation model quality in security domains.Fine-tuning & Adaptation: Own fine-tuning strategies for downstream security tasks: phishing detection, BEC classification, malware indicators, policy violation detection. Implement parameter-efficient techniques (LoRA, adapters, prefix tuning) for rapid task adaptation.Datasets & Data Quality: Design dataset generation and curation pipelines that preserve privacy while maintaining threat diversity. Implement synthetic data generation strategies to overcome security data scarcity. Ensure data quality checks, bias detection, and governance aligned with regulatory requirements (SOC 2, ISO 27001).Small Language Models & Edge Deployment: Develop efficient SLMs via quantization, pruning, and distillation for customer environments and edge devices. Optimize inference latency (<100ms) and memory (<2GB) without sacrificing accuracy.Evaluation & Testing: Build rigorous evaluation frameworks specific to security: adversarial robustness, false positive rates in production, attack coverage. Assess model robustness, bias, safety, and interpretability. Mentor teams and raise standards for AI safety and privacy-first development.Cross-functional Collaboration: Partner with Product to define LLM-driven features and translate requirements into ML problems.Core Technical ExpertiseTransformer architectures: BERT, GPT, T5, LLaMA, emerging models; ability to evaluate trade-offs between model familiesLarge-scale pre-training: data pipeline design, training efficiency, convergence optimization, compute cost managementParameter-efficient fine-tuning: LoRA, adapters, prefix tuning, prompt tuningModel compression & efficiency: quantization (INT8, FP8), pruning, knowledge distillation, low-rank factorizationDataset engineering: scalable data pipelines, quality assurance, privacy-aware annotation, synthetic data generationLLM evaluation & testing: task-specific metrics, adversarial testing, bias assessment, robustness evaluationRequired Qualifications (Must haves)PhD in ML, NLP, CS, or related field; or equivalent industry experience (10+ years at top-tier tech, research labs, or specialized ML companies) 10+ years in ML/AI with 5+ years focused on LLMs at scale (pre-training, fine-tuning, or deployment at 100M+ scale)Proven hands-on experience training, fine-tuning, and deploying LLMs in production with measurable impact (latency, cost, accuracy tradeoffs) Strong hands-on expertise with PyTorch and distributed training frameworks (FSDP, DeepSpeed, Ray, etc.) Experience in security, privacy, or safety-critical domains (security analytics, threat intelligence, fraud detection, privacy-preserving ML preferred)Nice to HavePublications in top-tier ML/NLP/security venuesProduction-scale experience with privacy-preserving MLSecurity domain expertise: malware detection, phishing/BEC classification, threat intelligence, anomaly detectionMoE, RAG, multimodal, or cross-lingual models experienceEdge/mobile deployment or embedded ML systems experienceInference optimization frameworks: ONNX, TensorRT, vLLM, llama.cpp, OllamaAlignment, RLHF, and responsible AI practices with production experienceExperience building or scaling ML teams from scratch