• Executes standard software solutions, design, development, and technical troubleshooting
  Writes secure and high-quality code using the syntax of at least one programming language with limited guidance
• Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications
• Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation
• Hands on code development to enable our AI/ML platform, ensuring robustness, scalability, and high performance.
• Adopt the best practices in software engineering, machine learning operations (MLOps), and data governance.
• Maintain consistent code check-ins every sprint to ensure continuous integration and development.
• Executes Platform engineering to enable the Gen AI platform and develop the Gen AI Use cases ,LLM fine tuning and multi agent orchestration.
  Communicate technical concepts and solutions effectively across all levels of the organization.
• Applies 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.
• Troubleshoot production issues, perform root-cause analysis, implement preventive improvements, and contribute through code reviews, standards enforcement, and mentoring.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Extensive practical experience with Python and AWS cloud services, including EKS, EMR, ECS, and DynamoDB.
• Advanced knowledge in software engineering, AI/ML, machine learning operations (MLOps), and data governance.
• Real-time model serving experience with Seldon
• Hands-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.
  • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.