Your future duties and responsibilities: Skill: pgvector,Vertex AI, FastAPI, Flask, Kubernetes Develops and optimizes AI applications for production, ensuring seamless integration with enterprise systems and front-end applications. Builds scalable API layers and microservices using FastAPI, Flask, Docker, and Kubernetes to serve AI models in real-world environments Implements and maintains AI pipelines with MLOps best practices, leveraging tools like Azure ML, Databricks, AWS SageMaker, and Vertex AI Ensures high availability, reliability, and performance of AI systems through rigorous testing, monitoring, and optimization Works with agentic frameworks such as LangChain, LangGraph, and AutoGen to build adaptive AI agents and workflows Experience with GCP, AWS, or Azure - utilizing services such as Vertex AI, Bedrock, or Azure Open AI model endpoints Hands on experience with vector databases such as pgvector, Milvus, Azure Search, AWS OpenSearch, and embedding models such as Ada, Titan, etc. Collaborates with architects and scientists to transition AI models from research to fully functional, high-performance production systems. Skills: Azure Search Flask Kubernetes
