Software Engineer III - Python, Data, AIML, Cloud

Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development

• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems

• Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture

• Contributes to software engineering communities of practice and events that explore new and emerging technologies

• Adds to team culture of diversity, opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years 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 preferably)

• Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages

• Overall knowledge of the Software Development Life Cycle

• 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

• Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds

Preferred qualifications, capabilities, and skills

• Experience with data, AWS and AIML engineering in commercial settings
• Experience working on Production scale AI/ML systems
• Practical experience with Kubernetes, EKS, Docker, Terraform, MLOps
• Prior exposure to LLMs, RAG, OpenSearch and vector databases
• Prior experience collaborating with data scientists