This is an exciting opportunity to leverage cutting-edge AI technology using RAG and LLM pipelines.
As a senior Machine Learning Engineer, you will be responsible for designing and implementing ML solutions focused on production-grade infrastructure. This includes containerized services deployed via Docker and Kubernetes.
You will also lead the system design and implementation of new use cases for our NLQ product by working with data pipelines and enhancing prompt engineering.
The ideal candidate will have 10+ years of experience in Machine Learning Engineering, with a strong emphasis on production-grade systems. Proficiency in Python and LangChain is required.
Additionally, the candidate should have experience with vector-based search techniques, RAG architectures, and Postgres database management.
AWS services and containerized deployments (Docker, Kubernetes) are also essential skills for this role.
We are looking for an expert who can drive infrastructure improvements and build new ML components from scratch where necessary. The ability to influence architecture decisions and promote ethical and compliant AI practices is also crucial.
The successful candidate will deliver measurable impact by leading the launch of at least one new use case within the first two months.