Role Summary :

We are looking for a highly skilled Backend Engineer with a specialized focus on AI/ML Infrastructure to architect and scale the systems powering our next-generation AI products. This is not a data science role, but a core engineering position focused on the productionization of AI. You will be responsible for building high-concurrency backend systems that support Search, Recommendation Engines, and RAG (Retrieval-Augmented Generation) pipelines. Your expertise will bridge the gap between raw models and user-facing applications, ensuring that embeddings, vector searches, and reranking modules operate with sub-second latency and high reliability. As a senior member of the team, you will own the end-to-end lifecycle of ML platform components, from feature stores to A/B testing frameworks, while mentoring junior engineers in distributed systems best practices.

Responsibilities :

- AI Product Infrastructure : Architect and maintain backend services for AI-driven features like intelligent search, recommendation systems, and AI assistants.

- RAG Pipeline Engineering : Design and optimize end-to-end RAG pipelines, including high-performance data ingestion, strategic chunking, indexing, and sophisticated retrieval/reranking logic.

- Vector Search Optimization : Implement and manage vector databases and search algorithms to ensure high-quality retrieval and relevance metrics.

- Distributed Systems Governance : Build resilient, distributed backend architectures using Go, Java, or Python, focusing on concurrency, rate limiting, and automated retry mechanisms.

- ML Platform Development : Develop and manage core ML platform components, including Feature Stores, automated training pipelines, and evaluation frameworks.

- Experimentation Frameworks : Design and execute A/B testing infrastructures to validate model improvements and backend optimizations in real-time.

- API Development & Design : Create robust, scalable APIs that serve as the interface between complex ML logic and front-end consumers.

- Quality & Metrics Tracking : Establish and monitor retrieval quality metrics (e.g., nDCG, MRR) to ensure the accuracy and efficiency of AI outputs.

- Reliability Engineering : Implement observability and monitoring for ML services to proactively identify bottlenecks in the inference or retrieval path.

Technical Requirements :

- Engineering Core : 6 to 10 years of experience building large-scale backend systems with a focus on reliability and distributed architecture.

- AI/ML Specialization : 2 to 3+ years of hands-on experience specifically on AI-integrated products (Search, Recommendations, or LLM-based apps).

- Search & Retrieval Fundamentals : Deep understanding of Embeddings, Vector Search, and the mechanics of reranking and retrieval quality.

- Language Proficiency : Expert-level coding skills in Go, Java, or Python.

- Distributed Systems : Strong knowledge of API design, concurrency models, and system reliability patterns.

- Education : Bachelors degree in Computer Science, Engineering, or a related technical field.

Preferred Skills (Good to Have) :

- Agentic Systems : Experience building Agentic Workflows involving tool/function calling and multi-step reasoning.

- Vector Ecosystem : Hands-on experience with FAISS, Milvus, Pinecone, or Elasticsearch.

- Infrastructure & DevOps : Experience with Kubernetes (K8s) and observability stacks like OpenTelemetry, Prometheus, and Grafana.

- Governance & Security : Familiarity with AI data privacy, security protocols, and data governance frameworks.

- Advanced Problem Solving : Strong ownership mindset with the ability to troubleshoot complex system-level bottlenecks in an AI context.