Were 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 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 Youll 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.
Requirements: 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).
This position is open to all candidates.