MLOps Team LeadAIניהול

Key responsibilities

Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence

Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives

Establish best practices, standards, and processes for ML infrastructure, deployment, and operations

Own technical decision-making for ML infrastructure architecture and tooling choices

Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks

Drive the design and implementation of robust model serving infrastructure using platforms like Triton, TorchServe, TensorFlow Serving, and KServe

Define inference architecture strategy optimized for ultra-low latency and high throughput

Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

Collaborate with research teams to bridge the gap between experimentation and production

Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability

Must have

Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)

7+ years of experience in MLOps, with 5+ years in a technical leadership role

Strong software engineering skills in Python, with experience in Bash and/or Go

Proven track record of building and leading high-performing MLOps or infrastructure teams

Experience building and designing MLOps infrastructure from the ground up

Deep experience with MLOps platforms (MLflow, WandB, etc.) and frameworks (PyTorch, TensorFlow, etc.)

Deep experience with model serving frameworks (Triton, TorchServe, TensorFlow Serving, KServe) for high scalability and low latency inference