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Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.\nJob responsibilities:\nExecutes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems\nDrives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.\nCreates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems\nDrive the AI / ML delivery best practices along with software engineering best practices.\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.\nDefine architecture for series of complex deliverables.\nGathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems\nProactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture\nRequired qualifications, capabilities, and skills:\nFormal training and certification on software engineering concepts and 5+ years applied experience. In addition, 2+ years of experience leading technologists to manage and solve complex technical items within your domain of expertise\nHands-on practical experience in system design, application development, testing, and operational stability\nExperience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages\nHands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.\nSolid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security\nGood knowledge of Machine Learning modelling as an engineer\nExperience in working with one or more programming language(s) and framework(s) (i.e., Python, PySpark, Big Data, Data pipeline, Machine Learning, etc.)\nStrong SQL skills with ability to validate features and data quality at scale\nExperience configuring and optimizing Spark clusters for processing large data.\nHands-on experience working with AWS services including EMR, EC2, S3, and CloudWatch\nWorking knowledge on Databricks in creating Data Pipelines, Machine learning models deployment. \nPreferred qualifications, skills, and capabilities:\nExpertise with programming languages like Java, python\nExperience in Cloud Technologies (i.e., AWS - 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