Overview
In this role you enable data-science models to run reliably in production by owning the ML infra and operations on AWS and Kubernetes. You will design, deploy, monitor, and scale production ML services, collaborating with Data Scientists who retain model development. You'll build CI/CD pipelines, IaC workflows, and robust APIs while enforcing security, observability, and cost-efficient operation. This position supports both existing products and new development, offering impact at scale and opportunities to mentor teammates.
Compensaciones / Incentivos competitive benefits and salaries
personal and professional development opportunities
flexibility
growth opportunities
collaborative culture
Responsabilidades
Lead complex MLOps projects with autonomy and judgment
Design, develop, test, and debug software for customer-facing and internal apps
Operate production ML services including deployment, scaling, and monitoring
Maintain Kubernetes deployments and AWS infrastructure for model-serving workloads
Develop and maintain CI/CD pipelines and infrastructure-as-code for model-serving systems
Create and maintain APIs, orchestration layers, and data interfaces for production models
Collaborate with Data Scientists to productionize models and resolve integration issues
Benchmark and stress-test ML/LLM services; ensure reliability and performance
Establish logging, metrics, alerting, dashboards, incident response, and cost-effective operation
Operate MLflow and Kubeflow for lifecycle, pipelines, and workflows
Evaluate new technologies to strengthen systems and enforce engineering standards
Mentor junior engineers and contribute to roadmaps and cross-team initiatives
Share MLOps expertise and stay current with industry developments
Develop domain expertise in at least one cybersecurity application area
Document technical approaches and decisions
Requisitos principales 7+ years in engineering or related roles with hands-on production operations
Strong cloud and softwar