Scoping and shaping AI platform initiatives for LS&H products, from early concepts through implementation, actively influencing technical direction and strategic decisions such as building in-house capabilities, leveraging cloud-native AI services, or adopting open-source and LLM-driven technologies.
Designing, developing, and maintaining backend services, including APIs, microservices, and ETL pipelines running in Azure or AWS.
Translating ideas, requirements, and problem statements from multiple teams into high-quality, reliable code and working proof-of-concepts.
Collaborating closely with Cloud Operations, Solution Architecture, Product Engineering, Product Management, and SMEs to understand needs and deliver technical solutions.
Building and iterating LLM-based or agentic AI applications, ensuring reliability, quality, and awareness of AI limitations.
Evaluating and integrating with applications written in other languages (e.g., Java, Typescript) when needed.
Leading the technical approach on assigned work by taking initiative and ownership from design through implementation.
Testing, validating, and continuously improving solutions through unit tests, automation, and iterative development.
Progressing projects from initial concept to prototype and contributing to the technical plans needed to move them toward production.
Working in an agile, fast-paced environment, adapting priorities based on business needs and contributing to a culture of continuous improvement.
Requirements
Bachelor’s degree in computer science, Software Engineering, or a related field, or equivalent experience.
At least 7 years of professional software engineering experience, including at least 4 years developing backend systems in Python (APIs, microservices, ETL pipelines).
At least 5 years of experience working in cross-functional, agile teams, collaborating with Product, Cloud Ops, Architecture, Engineering, and SMEs.
At least 3 years of experience working with LLMs or agentic AI applications, with demonstrable understanding of their reliability challenges.