• Improve and maintain CI/CD, deployment workflows, and environment management across backend, web, and internal services • Build, maintain and scale infrastructure across AWS and container based services • Improve monitoring, alerting, logging, dashboards, tracing, and runbooks • Work with engineers on safer deploys, rollback plans, and recovery from failures • Automate repetitive operational work and improve internal tooling • Maintain and improve infrastructure as code and deployment tooling • Help improve failover planning, recovery procedures, and backup/restore testing for critical systems • Support production systems and take part in on-call for critical services • Manage and scale infrastructure across AWS, ECS, Docker, PostgreSQL, Redis, Celery, and Go/Python-based services • Lead incident response and postmortems, and drive follow-up actions to reduce repeat issues • Improve reliability, resilience, and operational readiness across critical systems

• Design, implementation, maintenance, and performance of SQL Server, Azure SQL, Snowflake, and Microsoft Fabric data platforms • Ensure high availability, security, scalability, and data integrity • Support CI/CD pipelines and enterprise data gateway infrastructure • Monitor performance, identify bottlenecks, and implement tuning strategies • Configure, maintain, and support Fabric workspaces and artifacts • Deploy and manage Gateway Infrastructure • Provision and optimize Snowflake environments • Build and maintain YAML-based Azure Pipelines • Analyze and tune queries and proactive performance monitoring • Configure, maintain, and assist in testing disaster recovery plans • Collaborate with developers, data engineers, BI analysts, and architects

• Design, build, and maintain tools and systems that support release automation and deployment workflows. • Write clean, reliable, and concurrent code that supports distributed systems (e.g., build pipelines, deployment tooling). • Collaborate with cross-functional teams to understand and improve release quality and developer productivity. • Document technical designs, deployment practices, and operational procedures. • Participate in small-team design reviews and contribute practical engineering solutions.

• Responsible for leading and scaling technology functions that supports AI/ML workloads in a cloud-native environment. • Overseeing design, implementation, and maintenance of automated deployment pipelines, cloud infrastructure, and monitoring systems to ensure high availability, scalability, and security of AI-driven applications. • Collaborate closely with data scientists, machine learning engineers, and software developers to streamline model deployment, accelerate experimentation, and optimize operational practices. • Drive cloud cost optimization strategies, including effective resource allocation, right-sizing, and leveraging cloud-native tools to reduce operational expenses while maintaining performance and reliability. • Serve as a technical leader with deep knowledge of cloud platforms (AWS, Azure, or GCP), infrastructure-as-code, containerization, and AI workflows. • Mentor team and foster culture of continuous improvement, reliability, and efficiency.