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)