Your Role and Responsibilities

Take ownership of the stability, scalability, and performance of production-grade ML platforms. This involves designing and enhancing backend services, orchestration layers, and system integrations that support critical ML workflows. Ensure resilient system architectures in distributed environments to maintain high availability, tolerate faults, and facilitate smooth operations. Lead complex problem resolution and conduct in-depth root-cause analysis across application, data, and infrastructure layers to mitigate operational risks and reduce downtime. Champion reliable deployment and DevOps practices using Docker, Kubernetes (AKS), and CI/CD, ensuring adherence to configuration and release controls. Act as a senior technical point of contact for cross-functional teams and external vendors, translating intricate ML and business requirements into robust, scalable backend solutions.

The work model for this role is Hybrid.

Key Accountabilities:

• Oversee the design, improvement, and ownership of backend services for ML workflows, including model registries, deployments, and APIs. Ensure well-architected systems across service boundaries, data flow, scalability, fault tolerance, performance, and maintainability, providing architectural guidance to internal teams and vendors.
• Diagnose and resolve complex production incidents involving Spark jobs, Airflow pipelines, Azure ML runs, and AKS services through comprehensive root-cause analysis across application, infrastructure, data, and configuration layers. Enhance reliability, observability, and deployment stability.
• Design and maintain Dockerized services and Kubernetes (AKS) deployments. Contribute to CI/CD pipelines and establish best practices for configuration management, secrets handling, environment isolation, scaling strategies, upgrades, and rollback mechanisms.
• Serve as the primary technical liaison, collaborating with data scientists, ML engineers, business analysts, platform teams, and external vendors to translate business and ML workflow needs into scalable backend architectures. Coach less experienced engineers on system design and complex debugging techniques.

Qualifications:

• 3-5 years of experience in Backend Engineering, Platform Engineering, Senior ML Engineering, or a similar capacity.
• Proficient backend development skills in Python (or Java/Go), with hands-on experience in creating APIs and services.
• Strong understanding of system design principles, including distributed systems, microservices, scalability, fault tolerance, and reliability.
• Direct experience with Azure Machine Learning (AML) and Airflow, focusing on platform or operational responsibilities.
• Working knowledge of Azure ML pipelines, endpoints, and deployment patterns.
• Solid practical experience with Docker and Kubernetes (AKS).
• Proven ability to identify and resolve complex production issues spanning application, data, and infrastructure layers.