Machine Learning Platform Engineer

Role Overview

• Design, build, and operate scalable ML platform components including training infrastructure, feature stores, model registries, inference services, and end‑to‑end workflow orchestration.
• Develop cloud‑native, distributed systems and CI/CD pipelines that ensure reliable, reproducible, and continuously delivered ML model deployments.
• Implement and mature MLOps capabilities such as experiment tracking, data and model versioning, model evaluation, monitoring, and automated retraining.
• Establish best practices for model lifecycle management, testing, and deployment across development, staging, and production environments.
• Integrate observability into ML systems, enabling deep visibility into performance, drift, data quality, and inference reliability.
• Build and optimize cloud-based ML infrastructure on Azure, AWS, and/or GCP using Kubernetes, container orchestration, and infrastructure‑as‑code tools.
• Develop scalable batch and real‑time data pipelines that power feature generation, training workflows, and high‑performance model serving.
• Ensure security, compliance, and cost-effectiveness across ML environments in partnership with platform, architecture, and governance teams.
• Collaborate with data scientists and applied ML teams to translate modeling needs into robust, reusable, and self-service platform capabilities.
• Work with security, compliance, and architecture partners to uphold responsible AI, governance, and data protection standards.
• Drive developer productivity by promoting self‑service tooling, reusable components, documentation, and engineering best practices.
• Contribute to Agile delivery processes while championing automation, engineering excellence, and continuous improvement.

Requirements

• Strong software engineering background with experience building distributed systems or platform services
• Hands-on experience with machine learning workflows, MLOps tooling, and productionizing ML solutions
• Proficiency in Python and familiarity with ML libraries, frameworks, and backend development patterns
• Experience with cloud platforms and ML services, including Azure ML Studio, AWS SageMaker, and/or Google Vertex AI
• Exposure to cloud storage/data such as Azure Fabric/OneLake, AWS S3, and Google Cloud Storage (GCS)
• Experience with cloud-native scanning and security tools such as Azure Defender, Microsoft Purview, AWS Security Hub, Amazon Inspector, GCP Security Command Center, or equivalent services
• Strong understanding of technologies such as Kubernetes, Docker, CI/CD, Terraform/Infrastructure-as-Code, etc.
• Understanding of system design, API architecture, and scalable data/ML infrastructure
• Strong communication and cross-functional collaboration skills.
• 4+ years of experience in ML engineering, platform engineering, or equivalent (preferred).

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• Google Cloud Platform
• Kubernetes
• Python
• Terraform

Benefits

• Joining our team isn’t just a job — it’s an opportunity
• One that takes your skills and pushes them to the next level
• One that encourages you to challenge the status quo
• One where you can shape the future of protection while supporting causes that mean the most to you.