About the job
Your Role at Lila Sciences
We are in search of a Senior Software Engineer to become a pivotal member of our Applied AI team, contributing to the development of our next-generation AI-powered scientific platform. This position involves designing and refining backend systems, data pipelines, and AI integrations that facilitate intelligent, data-centric applications. You will operate at the crossroads of backend engineering and machine learning, ensuring that our platform scales effectively and supports state-of-the-art applied AI methodologies such as Retrieval-Augmented Generation (RAG), agentic AI, and large language model (LLM) integration.
This opportunity is perfect for individuals who excel at merging software engineering with applied AI, transforming research into production-level systems that foster genuine scientific breakthroughs. If you are driven by the challenge of building high-performing, sophisticated systems that maximize the utility and impact of AI, we would be eager to connect with you!
Your Contributions
- Applied AI Integration: Design and implement backend services and data pipelines that underpin advanced AI applications, including LLMs, RAG, and agentic frameworks.
- API & Service Development: Create high-performance APIs and microservices that facilitate seamless interactions between AI models, scientific tools, and user-facing applications.
- Data Pipeline Architecture: Architect and oversee scalable pipelines adept at managing structured, unstructured, and vectorized data for AI/ML workloads.
- Database & Knowledge Systems: Develop and optimize SQL, NoSQL, and vector databases to ensure low-latency AI retrieval and inference tasks.
- Cloud & Infrastructure: Utilize AWS, Kubernetes, and infrastructure-as-code (Terraform/CloudFormation) to construct resilient, production-ready AI platforms.
- Performance & Reliability: Identify system bottlenecks, optimize for efficiency and speed, and guarantee the reliability and fault-tolerance of AI-driven processes.
- Collaboration: Work closely with ML researchers, platform engineers, and scientists to translate models and algorithms into scalable, production-ready solutions.

