Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps
Designs and implements data engineering solutions, leveraging modern big data technologies
Contributes to software engineering communities of practice and events that explore new and emerging technologies
Embraces a passion for learning, problem-solving, creative thinking and a can-do attitude.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and proficient applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Proficient in coding in one or more languages- Python
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
Proven track record in system design, architecting and developing microservices, distributed systems and data-intensive applications
Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies
Practical experience developing Production-scale Cloud-native data engineering solutions in commercial environments
Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack
Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds and Overall knowledge of the Software Development Life Cycle
Preferred qualifications, capabilities, and skills
Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector
Experience working on LLM applications or other AI/ML systems
Practical experience with Kubernetes, EKS, Docker, MLOps
Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases
Prior experience collaborating with data scientists
