Senior ML Engineer

About Us: Zenity is the first and only holistic platform built to secure and govern AI Agents from buildtime to runtime. We help organizations defend against security threats, meet compliance, and drive business productivity. Trusted by many of the world’s F500 companies, Zenity provides centralized visibility, vulnerability assessments, and governance by continuously scanning business-led development environments. We recently raised $38 million in a Series B funding, solidifying our position as a leader in the industry and enabling us to accelerate our mission of securing AI Agents everywhere.

About the role: As a Senior MLOps Engineer at Zenity, you will design, build and scale our machine learning infrastructure. You will lead the end to end model lifecycle, from data and training to deployment and monitoring, ensuring reliable and high performance ML systems in production. You will work closely with our research and engineering teams to bring advanced AI models into production and help shape the future of our AI capabilities.

Requirements:

• 6+ years of hands-on experience building, deploying, and operating ML pipelines and distributed model-serving systems at scale.
• Production experience with model lifecycle management including dataset management, training, experimentation, versioning, deployment, and monitoring using tools like MLflow, Weights & Biases, SageMaker or similar.
• Strong background in model optimization and inference acceleration for NLP/Vision models (e.g., vLLM, ONNX Runtime, TensorRT, quantization, and distillation).
• Proven ability to refactor research code into production-ready services with automated testing and continuous integration (CI/CD).
• Proficiency in Python and infrastructure-as-code (Terraform, CloudFormation, or similar).
• Hands-on experience with Kubernetes-based deployments, Kubeflow, Ray or similar for scalable model serving.
• Experience with cloud-native ML services (AWS SageMaker, GCP Vertex AI, or Azure ML).
• Knowledge of stream and batch processing frameworks (Kafka, Flink, Spark Structured Streaming) is an advantage.
• Experience with dataset management and data generation
• Strong communication, ownership, and problem-solving mindset.