Senior MLOps Engineer

Noma Security is defining how the world secures AI-building the trust layer for the era of intelligent systems.

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).

Bonus Points

• Experience in fast-paced, early-stage, product-led startups.
• Background in building or operating internal ML platforms.
• Knowledge of evaluation frameworks for LLM quality, robustness, or observability.

Nice to Haves

• 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.

💙 Why Noma?

Noma Security is a high-velocity, mission-driven startup shaping the future of AI-native data security.

As part of a small and highly technical R&D team, your work will directly influence how organizations build, deploy, and secure AI systems at scale.

You’ll join us at a formative moment—your architecture, tooling, and decisions will shape the ML foundation of the company for years to come.