AI Infrastructure Engineer

Description Position : AI Infrastructure Engineer Experience Required : 4 to 6 years Employment Type : Full-Time About The Role We are seeking a strong AI Engineer with a heavy focus on infrastructure and production-grade AI systems. This role is ideal for someone who enjoys building scalable AI backends, setting up robust data and MLOps pipelines on GCP, and deploying agentic AI applications end-to-end. If you are passionate about AI infrastructure, cloud-native systems, and real-world AI deploymentnot just experimentationthis role is for you. Key Responsibilities • Design and build AI infrastructure using Python or Rust with a strong focus on performance and scalability. • Set up end-to-end data pipelines from APIs to storage layers such as AlloyDB or CloudSQL. • Design and manage data models to support AI workloads and application requirements. • Build and maintain GCP-based MLOps pipelines for training, deployment, monitoring, and versioning. • Develop and deploy AI applications using Vertex AI, including RAG-based and agentic workflows. • Implement agentic tool usage and orchestration for multi-step AI workflows. • Build FastAPI-based backend services and token-streaming endpoints for real-time AI responses. • Ensure reliability, observability, and security of AI systems in production environments. • Collaborate closely with product, data, and frontend teams to deliver scalable AI solutions. Skills & Requirements • Strong programming background in Python or Rust. • Hands-on experience with GCP, especially Vertex AI and cloud-native services. • Solid experience in MLOps, including model deployment, monitoring, and pipeline automation. • Strong understanding of data modeling and backend data pipelines. • Experience setting up API-driven data ingestion and storage using AlloyDB or CloudSQL. • Hands-on experience with FastAPI and building production-grade APIs. • Practical experience with RAG architectures and agentic AI workflows. • Understanding of token streaming, latency optimization, and scalable AI serving. Nice To Have • Experience with containerization and orchestration (Docker, Kubernetes). • Familiarity with vector databases and embedding pipelines. • Exposure to LLM observability, evaluation, and cost optimization strategies. • Prior experience building AI systems at scale in production environments. (ref:hirist.tech)