Description

Candidate Skill:    Backend, RAG, AI/ML, Distributed Systems, APIs, Vector DB, Kubernetes, Observability, Python/Java
Experience:    5+ Years

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. Key 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 Implement observability (OpenTelemetry, Prometheus, Grafana, tracing) Ensure secure and reliable production systems Required Skills Strong backend engineering experience (6–10 years) Hands-on experience in AI/ML-based products (RAG, search, recommendations) Deep understanding of RAG pipelines (chunking, embeddings, indexing, retrieval) Knowledge of ML retrieval concepts (vector similarity, ranking, evaluation metrics) Strong distributed systems design skills Experience with API design, concurrency, retries, fault tolerance Familiarity with ML platform components (feature stores, pipelines, evaluation jobs) Proven ability to deliver end-to-end systems independently Good to Have Experience with agentic AI systems and tool/function calling Knowledge of vector databases (FAISS, Milvus, Pinecone, Elasticsearch) Exposure to model serving architectures Hands-on experience with Kubernetes and cloud infrastructure Strong understanding of observability (OpenTelemetry, Prometheus, Grafana) Experience with data privacy and security controls Soft Skills Strong problem-solving and system design mindset Ability to work independently and take ownership Good communication and collaboration skills

Key Skills

Education

Any Graduate