Description

You will architect, design, and optimize Python-based Retrieval-Augmented Generation (RAG) systems for production deployment.

Responsibilities

• Build and maintain vector databases and embedding pipelines using Redis or similar high-performance stores.
• Develop and manage GPU-accelerated pipelines for embedding generation and model vectorization.
• Design and operate Kubernetes (K8s) environments to scale distributed microservices and model workloads.
• Integrate retrieval and generation components into larger AI/LLM ecosystems ensuring high throughput and reliability.
• Own performance tuning, profiling, and system observability for millisecond-level responsiveness.

Required Skills

• 5+ years of experience in Python software engineering for production systems.
• Deep expertise with Redis, Kubernetes, and GPU-based computation (CUDA, PyTorch, or TensorFlow).
• Proven experience designing and deploying vector databases, embeddings, and RAG architectures.
• Strong knowledge of FastAPI, asyncio, and microservice architectures.
• Skilled with Docker, Helm, and cloud-native stacks (AWS, GCP, Azure).
• Demonstrated ability to deliver high-quality code under pressure and within tight deadlines.
• Experience with Kafka or Spark.

Preferred Skills

• Familiarity with LangChain, LlamaIndex, or other RAG frameworks.
• Experience with monitoring and observability tools (Prometheus, Grafana, OpenTelemetry).

Key Skills

Education

Bachelor's degree