Senior Data Scientist ( Remote)

Cognizant is a leading digital company, and they are seeking a Senior Data Scientist to serve as an Architect specializing in cloud native DevOps and ML Ops solutions. The role involves designing scalable automation for application delivery and machine learning workflows to enhance reliability, security, and speed of software releases.

Responsibilities:

• Design robust end to end cloud architectures that integrate AWS CodePipeline CodeDeploy CodeCommit CodeBuild and CloudFormation to deliver secure and highly automated application release workflows that improve deployment speed and quality across business critical systems
• Define and implement standard patterns for infrastructure as code using Terraform and AWS CloudFormation enabling consistent reproducible and compliant environments that reduce manual effort and operational risk for development and operations teams
• Develop efficient ML Ops architectures that streamline model training validation deployment and monitoring so that machine learning solutions move reliably from experimentation to production and deliver measurable value to customers and communities
• Coordinate closely with application developers data scientists and operations teams to translate complex functional and nonfunctional requirements into practical cloud and DevOps designs that balance performance scalability security and cost efficiency
• Establish and refine branching strategies and workflow conventions in Git repositories to maintain clean version control practices that support frequent changes traceability and collaboration in a hybrid work environment without disrupting delivery timelines
• Optimize continuous integration and continuous delivery pipelines across multiple products by configuring automated builds tests security checks and approvals so that releases are predictable auditable and aligned with enterprise governance expectations
• Create detailed architectural diagrams standards and documentation for cloud deployments pipelines and ML Ops processes ensuring that technical decisions are transparent reusable and easy to onboard for new team members and stakeholders
• Evaluate existing delivery pipelines infrastructure configurations and ML workflows to identify bottlenecks and risks then propose pragmatic improvements that increase reliability resilience and resource efficiency across environments
• Collaborate with platform security and compliance stakeholders to embed security by design in CodePipeline CodeDeploy and Terraform based solutions ensuring that encryption access controls and audit mechanisms protect sensitive data and services
• Guide teams in effective use of AWS managed services and DevOps tooling by conducting design reviews sharing best practices and providing hands on support that helps project squads adopt automation and cloud capabilities with confidence
• Monitor pipeline performance build times deployment success rates and ML model operational metrics then use data driven insights to tune architectures and processes for continuous improvement and sustainable long term operations
• Contribute to enterprise wide reference architectures and reusable templates for AWS DevOps and ML Ops so that the organization scales innovation consistently and brings reliable digital solutions to market faster with reduced duplication of effort
• Align architectural decisions with the company purpose and sustainability goals by favoring efficient resource usage resilient systems and ethical ML practices so that technology solutions positively impact clients employees and broader society