Senior MLOps Engineer

Description Join Noma Security and help shape the future of AI security in a fast-paced, high-impact environment. Noma is building the first comprehensive AI Security and Governance Platform, helping organizations adopt AI and LLMs with confidence and control. As AI becomes core to modern enterprises, we provide seamless visibility and protection across the entire AI lifecycle. Our team works at the intersection of AI, security, and cloud, shaping a new category in a rapidly evolving market. At Noma, you’ll have the opportunity to make a meaningful impact while working with exceptional people on technology that truly matters. We’re looking for a Senior MLOps Engineer to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights. As we scale our AIDR product and expand deeper into model-driven security intelligence, we are looking for a Senior MLOps Engineer to own the infrastructure, tooling, and operational foundations that power our NLP and LLM training, evaluation, and deployment workflows. You will architect and operate the systems that enable us to train, fine-tune, deploy, and monitor models at scale making ML at Noma reliable, fast, cost-efficient, and production-ready. This is a high-visibility, high-impact role where you will partner closely with DevOps, Backend, Data, and Product to establish world-class ML infrastructure from the ground up. 🛠️ What You’ll Do Build & Scale ML Pipelines Design, build, and maintain pipelines for training, fine-tuning, evaluating, and deploying NLP and LLM models across GPU and CPU environments. Establish LLM-Focused CI/CD Implement automated CI/CD workflows for ML models, including benchmarking, testing, performance gating, and production deployment. Optimize Runtime & Inference Select and optimize serving frameworks for low-latency, high-throughput inference, ensuring reliability and scalability. Own ML Infrastructure Manage training environments, experiment tracking, model registries, artifact versioning, and distributed training systems. Operational Excellence Monitor and optimize production models for performance, cost efficiency, availability, and observability. ✅ Requirement for success: 5+ years in software engineering, MLOps, or ML engineering with hands-on experience deploying ML models to production. Strong Python fundamentals and deep understanding of transformer architectures, tokenization, and NLP frameworks (PyTorch, HuggingFace). Proven experience deploying and scaling LLMs for real-time inference—ideally on platforms like SageMaker, Vertex AI, or similar. Expertise in GPU optimization, distributed training, and CPU-based inference optimization. Strong cloud and Kubernetes background (EKS/GKE/AKS, Helm, Terraform, CI/CD for ML). 🎯 Nice to Haves Background in building or operating internal ML platforms. Knowledge of evaluation frameworks for LLM quality, robustness, or observability. Experience working with data-driven ML operations, cost optimization, and model observability. Understanding of security implications in ML pipelines. Familiarity with multi-model orchestration, vector DBs, or retrieval pipelines. 💙 Perks & Benefits Located in a prime area with easy access to public transportation, near the Hashalom train station and the Light Rail. A bright, spacious, dog-friendly office that makes you smile the moment you walk in 🐶 Team bonding activities, company retreats, happy hours, and holiday gifts Pilates classes at the office 💪🧘‍♀️ Never hungry: Monthly lunch budget, dinners provided, and a fully stocked kitchen 🍽️ A warm, supportive, and people-first company culture Pension & Keren Hishtalmut (advanced training fund) A global work environment with teams across Tel Aviv, the US, the Netherlands, and London Options that let you share in Noma’s growth and success