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.