About Us
“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.
WHY JOIN CAPCO?
You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.
MAKE AN IMPACT
Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.
#BEYOURSELFATWORK
Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.
CAREER ADVANCEMENT
With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.
DIVERSITY & INCLUSION
We believe that diversity of people and perspective gives us a competitive advantage.
MAKE AN IMPACT
Key Responsibilities -Design, develop and deploy AI/ML solutions on cloud platform(AWS)Build and optimize data pipelines using Databricks and PySpark for large-scale data processingImplement machine learning models and integrate them into production environmentsCollaborate with data scientists and engineers to ensure scalable and efficient solutionsMonitor and maintain cloud-based ML workflows for performance and cost optimizationMaintain and support current legacy system in Unix and sql , pl/sql environmentDevelop high quality, secure and scalable data pipelines using spark, Java/Scala on object storage and HadoopFollow MasterCard Quality Assurance and Quality Control processesLeverage new technologies and approaches to innovating with increasingly large data setsWork with project team to meet scheduled due dates, while identifying emerging issues and recommending solutions for problemsPerform assigned tasks and production incident independentlyContribute ideas to help ensure that required standards and processes are in place and actively look for opportunities to enhance standards and improve process efficiencyAll About You
Hands-on experience with cloud platform(AWS) for AI/ML workloads.Strong proficiency in Python and PySpark for data engineering and ML tasks.Solid understanding of Databricks for big data processing and ML model deployment.Knowledge of data architecture, distributed computing and performance tuning.Familiarity with CI/CD pipelines for ML models.Should have experience of working in Unix environmentExperience in Data Engineering and implementing multiple end-to-end DW projects in Big Data environmentExperience of building data pipelines through Spark with Java/Scala on Hadoop or Object storageExperience of working with Databases like Oracle, Netezza and have strong SQL knowledgeExperience of working on Nifi will be an added advantageExperience of working with APIs will be an added advantageUnderstanding of containerization (Docker, Kubernetes) added advantageExperience of working in Agile teamsStrong analytical skills required for debugging production issues, providing root cause and implementing mitigation planStrong communication skills - both verbal and written – and strong relationship, collaboration skills and organizational skillsAbility to multi-task across multiple projects, interface with external / internal resources and provide technical leadership to junior team membersAbility to be high-energy, detail-oriented, proactive and able to function under pressure in an independent environment along with a high degree of initiative and self-motivation to drive resultsAbility to quickly learn and implement new technologies, and perform POC to explore best solution for the problem statementFlexibility to work as a member of a matrix based diverse and geographically distributed project teams
