Job Summary:

You are a systems-oriented engineer passionate about building reliable, scalable infrastructure for AI and ML workloads. You have deep expertise in cloud platforms, containerization, and distributed systems. You thrive on solving complex operational challenges and are motivated by enabling engineering teams to ship reliably and efficiently. You constantly seek to improve systems for reliability, observability, and developer experience.
The pace of our growth is incredible – if you want to build world-class infrastructure for cutting-edge video AI systems at scale, join us!

About the team:

Join our Video CoE as a Senior DevOps Engineer and build the infrastructure that powers next-generation video AI systems. You'll architect and maintain systems that enable ML engineers to rapidly iterate and deploy state-of-the-art video understanding models at massive scale. This role combines deep systems expertise with exposure to cutting-edge AI technology, offering the opportunity to shape how modern video AI infrastructure is built and deployed.

Key Responsibilities:

Design and implement robust, scalable cloud infrastructure for deploying video AI models and services
Build and maintain Kubernetes clusters and containerized deployment pipelines optimized for GPU workloads
Establish monitoring, logging, and observability systems for AI/ML production services
Develop infrastructure-as-code practices and automation for rapid, reliable deployment
Optimize cloud costs while maintaining performance and reliability standards
Work with ML engineers to develop efficient MLOps pipelines for model training, evaluation, and serving
Establish and maintain security, compliance, and data governance practices for AI systems
Mentor engineering teams on infrastructure best practices and cloud architecture patterns
Drive incident response, postmortems, and continuous improvement of system reliability

Skills and attributes for success:

4+ years of infrastructure, DevOps, or cloud platform engineering experience
Deep hands-on expertise with Kubernetes and container orchestration (Docker, Kubernetes)
Expert proficiency in at least one major cloud platform (AWS, GCP, or Azure)
Strong programming background in Python, Go, or similar languages
Deep understanding of distributed systems, networking, and storage concepts
Experience with GPU infrastructure, CUDA, or other accelerated computing environments
Hands-on experience with ML infrastructure tools and frameworks (Kubeflow, MLflow, Ray, or similar)
Strong understanding of infrastructure-as-code tools (Terraform, CloudFormation, Helm)
Experience with CI/CD pipelines and automation frameworks
Passion for monitoring, logging, and observability – experience with Prometheus, ELK, Datadog, or similar
Experience with database and storage systems optimization for large-scale workloads

Preferred education and experience:

Bachelors/Masters in Computer Science or a related field with 4-7 years of professional experience.