About the Role
+ Design and implement high-performance RESTful or GraphQL APIs to expose AI model functionality to front-end applications and external services.
+ Integrate Large Language Models (LLMs) and custom machine learning models into backend architectures, often using frameworks like LangChain, LangGraph, or FastAPI.
+ Build and maintain data ingestion pipelines and vector databases (e.g., Pinecone, Weaviate) to support Retrieval-Augmented Generation for AI agents.
+ Deploy and orchestrate containerized services using Docker and Kubernetes on cloud platforms like AWS, GCP, or Azure.
+ Implement real-time monitoring and alerting for AI system health, including tracking model performance, latency, and drift.
+ Design testing strategies for non-deterministic AI outputs, including the implementation of guardrails and safety constraints.
+ Own the full development lifecycle for services in a production SaaS environment. This includes ...
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