Overview

Candidate Skill: Backend, RAG, AI/ML, Distributed Systems, APIs, Vector DB, Kubernetes, Observability, Python/Java

Responsibilities

• AI/ML Platform Development
• Build and scale AI-powered backend systems (search, recommendations, assistants, RAG) Design and implement end-to-end RAG pipelines (ingestion ? embeddings ? retrieval ? ranking) Develop hybrid retrieval and re-ranking systems Backend & Distributed Systems
• Design robust APIs and backend services with scalability and fault tolerance
• Implement idempotency, concurrency control, retries, rate limiting, circuit breakers
• Ensure multi-tenant system reliability ML Systems & Data Pipelines
• Build and manage streaming and batch pipelines
• Work with feature stores, metadata systems, and offline evaluation jobs
• Implement A/B testing and experimentation frameworks Evaluation & Optimization
• Define and track retrieval quality metrics (Precision, Recall, nDCG, MRR) Monitor data drift and system performance
• Optimize latency, scalability, and cost Deployment & Observability
• Deploy and manage services using Kubernetes and cloud platforms

Qualifications

• Job Description:
• We are hiring Applied AI Engineers (Backend) to build scalable AI/ML-powered platforms and products. This role focuses on designing production-grade backend systems for search, recommendations, ranking, and RAG-based AI applications with strong emphasis on performance, reliability, and evaluation.