About the Role
As a DevOps leader, you will be responsible for the design, development, and management of DevOps and MLOps capabilities to enable the delivery of end-to-end engineering and machine learning programs for our clients. The teams you will be guiding will work with analytics consultants, data scientists, data engineers, and ML engineers to:
- Create custom solutions/demos/blueprints for a variety of client business problems.
- Design, Implement, and Configure tools/templates/platforms for infrastructure provisioning, data/ML model pipeline release, deployment, and orchestration.
The team of technical leads and architects (and in turn the teams) that you lead will:
- Determine user needs by analyzing technical requirements.
- Design technical solution architecture to meet user requirements.
- Design and configure integrated build, test, deploy, and release pipelines.
- Implement observability through monitoring and logging, drift detection, etc.
- Implement resource provisioning as per the deployment architecture.
- Infrastructure automation through tools and templates.
- Maintain project structure and pipelines.
- Write well-designed, testable, efficient code by using best software development practices.
- Implement continuous monitoring and support remediation.
As a DevOps/MLOps practice leader, you will also be responsible for:
- Lead & grow the DevOps/MLOps practice in India by bringing in the right talent, working with internal support teams.
- Identify & enable SMEs to support delivery & competency for our clients.
- Manage & drive Tiger’s technical capability development such as PoV, IP/Asset building, etc.
- Train and develop talent to support client program requirements.
Required Skills, Competencies & Experience
- 12+ years of relevant experience in a product or consulting services environment.
- Experience with most of the below:
- Setting up and using CI/CD through various tools and technologies e.g., GitHub, Jenkins.
- Cloud Managed DevOps toolchain (e.g., Azure DevOps).
- Deployment through Docker and Kubernetes including cloud managed services (e.g., AWS EKS).
- Programming languages. Ideally Python. Strong experience in another programming language like Java/C++ is acceptable.
- Infrastructure automation through various tools/technologies e.g., Terraform, ARM, CloudFormation etc.
- ML Model serving and orchestration using Kubeflow.
- In-depth understanding of at least one cloud platform. In particular, good understanding of security and access control models.
- Good hands-on experience with Kubernetes stack in production. Should have experience managing and scaling microservices in production.
- Good understanding of ML Model concepts such as data drift, concept drift, hyper parameter tuning.
- RESTful services, API design, and development.