Senior ML Ops Engineer

Key Responsibilities:

• Design, implement, and maintain scalable infrastructure for ML and GenAI applications.
• Deploy, operate, and troubleshoot production ML pipelines and generative AI services.
• Build and optimize CI/CD pipelines for ML model deployment and serving.
• Scale compute resources across CPU/GPU/TPU/NPU architectures to meet performance requirements.
• Implement container orchestration with Kubernetes for ML workloads.
• Architect and optimize cloud resources on GCP for ML training and inference.
• Setup and maintain runtime frameworks and job management systems (Airflow, KubeFlow, MLflow).
• Establish monitoring, logging and alerting for ML systems observability.
• Collaborate with data scientists and ML engineers to translate models into production systems.
• Optimize system performance and resource utilization for cost efficiency.
• Develop and enforce MLOps best practices across the organization.

Qualifications:

• Bachelor's degree in computer science, Information Technology, or a related field (or equivalent experience). 
• 5+ years of overall software engineering experience.
• 3+ years of focused experience in MLOps or similar ML infrastructure roles.
• Strong experience with Docker container services and Kubernetes orchestration.
• Demonstrated expertise in cloud infrastructure management, preferably on GCP (AWS or Azure experience also valued).
• Proficiency with workflow management and ML runtime frameworks such as Airflow, Kubeflow, and MLflow.
• Strong CI/CD expertise with experience implementing automated testing and deployment pipelines.
• Experience with scaling distributed compute architectures utilizing various accelerators (CPU/GPU/TPU/NPU).
• Solid understanding of system performance optimization techniques.
• Experience implementing comprehensive observability solutions for complex systems.
• Knowledge of monitoring and logging tools (Prometheus, Grafana, ELK stack).
• Proficient in at least two of the following: Shell Scripting, Python, Go, C/C++
• Familiarity with ML frameworks such as PyTorch and ML platforms like SageMaker or Vertex AI.
• Excellent problem-solving skills and ability to work independently
• Strong communication skills and ability to work effectively in cross-functional teams.