Senior Software Development Engineer (DevOps) - Video Insights
Jiostar Open · verified Oct 24, 2026 At a glance
Senior DevOps & Release role at Jiostar. Bengaluru.
Pay not stated DevOps & Release salary
devopsroles summary, based on the employer's posting.
What you'll do
- Build cloud infrastructure for video AI models and services, keeping it scalable and robust
- Operate Kubernetes clusters and tune container delivery pipelines for GPU workloads
- Set up monitoring, logs and observability for production AI and ML services
- Automate deployments, manage cloud spending and work with ML engineers on model pipelines
- Guide teams on infrastructure practices and lead incident reviews to improve reliability
What you bring
- At least four years in infrastructure, DevOps or cloud platform engineering
- Deep practical Kubernetes and container orchestration skills, plus expertise in a major cloud platform
- A strong programming foundation and knowledge of distributed systems, networking and storage
- Experience with GPU computing, ML infrastructure, infrastructure-as-code, CI/CD and observability tools
Who this fits
This senior role suits an infrastructure engineer who wants to support video AI and ML workloads at scale. The stated education and experience background is a computer science degree or related field, with four to seven years of professional experience.
From the employer
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.
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