Our innovative engineering solutions, built to scale with agile practices and design thinking, improve business efficiencies.
Interested in joining the Synechron Engineering team?
Providing leading edge platform, application solution architecture and design that enable our clients to stay relevant with industry standards, best practices, and be future compliant.
Bringing cutting-edge solutioning for new requirements using the best in architecture, data, service and design patterns, while complying with business architecture and security requirements.
Solutioning the next generation application architecture to bring it in line with the enterprise architecture using the latest architectural principles.
Evaluating new implementation of technologies and enterprise deployment. Building accelerators to combine technology and domain.
Building models and facilitating storytelling with data. Designing data architecture using modern concepts of streaming, messaging and batch processing.
Using Artificial Intelligence, Business Process Management and Robotic Process Automation to ingest, store and manipulate data in proper proportions to realize business improvement and achieve efficiencies.
Providing customized enterprise consulting for optimal automation requirements.
Strategically improving automation operations by providing expertise from conception through support and maintenance.
Providing engineering expertise for process governance and digital transformation.
Providing business intelligence and real-time data analytics. Establishing a series of metrics used to monitor automation progress, focusing on primary areas.
Offering customized services to meet all of the testing needs across domains, technologies, and applications. Creating centralized solutions that combine people, processes, tools, and infrastructure into a shared services function. Tangible business benefits include improved quality, faster time to market and reduced testing costs.
Framework and test development customized for Banking Applications.
Offering different types of testing to cover end-to-end automation.
Facilitating a process-driven approach for QA automation.
Implementing Automated Functional, Regression and Smoke Testing. Leveraging a Framework and Codeless approach for enterprise-level automation.
Deploying cutting-edge microservices architecture-based development for scalable, robust solutions with end-to-end observability focused on integrating larger and complex applications. Revamping enterprise-level API development, migration, and maintenance.
Designing resilient, scalable, interoperable, non-overlapping services.
Developing single responsibility microservices using the best-in-approach suitable microservices design patterns.
Continuous integration & build for faster build process and test verifications. Cost-optimized, Automated & Reliable Deployments.
Effectively tracking and monitoring service health and performance.
Engaging a combination of software development and IT operations to reduce the time to market and increase efficiency. Enabling improved process automation and application delivery through collaborative environments.
Using efficient agile development methodologies.
Enabling quick turnaround to the market by reducing delay and improving the rate of production releases.
Using best practices to plan and measure business priorities, and ensuring controls to monitor and alert.
Transformation of development operations with security and data quality right from the initiation of the development lifecycle.
Employing best-in-class methods for extracting insights - streamlining automated decision-making processes. Our standards and practices are based on years of experience building high-performant Artificial Intelligence and Data Science solutions.
Transforming businesses through better cost management and improved operational excellence. Assisting stakeholders to drive decisioning based on data.
Deriving metrics with the right insights from various business data points in order to identify failures and actions to predict accurate future insights.
Implementing Machine Learning model training, testing and operationalization.
Designing a series of architectural principals that provide highly sought-after capabilities of Data Science models deployment and tracking in production.
