Engineer Jobs

About The Position

Requirements

• 2+ years of experience in machine learning engineering, data engineering, software engineering, or a related technical role.
• Hands-on experience supporting production or near-production ML systems.
• Bachelor’s degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience.
• Solid hands-on experience with the GCP ecosystem, particularly Vertex AI components such as Workbench, Pipelines, and Model Registry.
• Proficiency with modern ML frameworks, including PyTorch or similar technologies.
• Experience with containerization tools, especially Docker, for automated builds and deployments.
• Practical experience managing data processing workflows using Apache Spark and Airflow.
• Understanding of MLOps best practices, including model deployment, monitoring, training workflows, inference support, and pipeline reliability.
• Strong problem-solving skills with the ability to troubleshoot, maintain, and optimize ML pipelines in a production environment.
• Collaborative mindset with the ability to execute technical tasks reliably under the guidance of a senior architect.

Nice To Haves

• Familiarity with real-time model serving and infrastructure tools such as Triton Inference Server and Terraform is highly preferred.
• Prior experience supporting high-traffic digital platforms or consumer-facing products.
• Experience with Triton Inference Server, Terraform, or similar infrastructure and real-time serving tools.
• Experience working in staff augmentation, consulting, or fast-moving client-facing environments.
• Strong interest in building reliable, production-grade ML systems rather than only experimental or research-focused models.

Responsibilities

• Support the design, deployment, monitoring, and maintenance of machine learning models in a high-traffic production environment.
• Maintain, troubleshoot, and optimize end-to-end ML pipelines from raw data ingestion through offline and online model evaluation.
• Execute daily MLOps tasks, including model training, inference support, pipeline monitoring, and deployment maintenance.
• Work with tools such as GCP, Vertex AI, Spark, Airflow, Docker, PyTorch, and related MLOps technologies.
• Build and manage automated containerized deployments to support continuous model operations.
• Partner closely with the Staff ML Architect and other ML Engineers to ensure models are reliable, scalable, and production-ready.
• Help identify and resolve performance, reliability, and scalability issues across ML workflows and infrastructure.