Role Overview

We are seeking an experienced Backend &

- Agentic AI Engineer with 79 years of backend engineering experience across Python and/or Node.js, including at least 2.5 years of hands-on experience building production-grade GenAI, RAG, semantic layer, and agentic AI systems. The ideal candidate can design, build, scale, observe, evaluate, and continuously improve AI-enabled platforms that operate reliably in enterprise environments.

Experience Requirements

- 7–9 years of professional backend engineering experience, preferably building scalable APIs, services, data-intensive platforms, and distributed systems.

- Solid hands-on development experience in Python and/or Node.js.

- Minimum 2.5 years of practical experience in Agentic AI, Generative AI, RAG systems, semantic search, semantic layer design, or LLM-powered enterprise applications.

- Proven experience taking AI-enabled systems from concept to production, including reliability, security, monitoring, and performance considerations.

Key Responsibilities

- Design, build, and scale backend systems that support GenAI, RAG, semantic layer, and agentic AI use cases.

- Develop robust services and APIs using Python and/or Node.js with strong attention to performance, maintainability, and production readiness.

- Build semantic layers that enable structured access to enterprise knowledge, business entities, metadata, and domain concepts.

- Implement RAG pipelines including document ingestion, chunking, embeddings, indexing, semantic search, hybrid retrieval, ranking, grounding, and response generation.

- Engineer agentic workflows involving planning, tool usage, orchestration, memory, task routing,



and multi-step reasoning patterns.

- Define and implement guardrails for safety, security, prompt injection protection, hallucination reduction, policy compliance, and controlled tool execution.

- Build observability modules for AI systems, including tracing, logging, metrics, prompt/response monitoring, retrieval quality tracking, latency, cost, and failure analysis.

- Evaluate and improve system performance using offline and online evaluation methods, golden datasets, retrieval metrics, LLM quality metrics, feedback loops, and experimentation.

- Collaborate with product, data science, platform, and business stakeholders to translate complex requirements into scalable technical solutions.

Technical Skills

- Languages: Python, Node.js, TypeScript/JavaScript.

- Backend Engineering: REST APIs, microservices, asynchronous processing, distributed systems, caching, queues, service reliability, and scalable system design.

- GenAI &
- Agentic AI:

LLM integration, prompt engineering, tool calling, agent orchestration, multi-agent workflows, planning patterns, and AI workflow frameworks.

- RAG &

- Semantic Systems:

embeddings, vector databases, semantic search, hybrid search, metadata filtering, re-ranking, knowledge retrieval, grounding, and semantic layer design.

- Cloud &

- Deployment:





containerized services, CI/CD, cloud-native deployment patterns, secure API integration, and production monitoring.

- Observability &

- Evaluation:

AI tracing, telemetry, evaluation datasets, retrieval precision/recall, hallucination tracking, response quality scoring, latency/cost optimization, and continuous improvement loops.

Must-Have Capabilities

- Strong backend engineering foundation with proven ability to build scalable, secure, and maintainable systems.

- Hands-on implementation experience with semantic layers, RAG pipelines, and enterprise knowledge retrieval systems.

- Practical exposure to agentic engineering, including workflow orchestration, tool integration, and controlled autonomous execution.

- Ability to define guardrails and governance mechanisms for safe and compliant AI behavior.

- Ability to instrument AI systems with observability, diagnostics, monitoring, and feedback capture.

- Ability to evaluate AI system quality and improve retrieval accuracy, response relevance, latency, reliability, and cost efficiency.

Good-to-Have Skills

- Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar orchestration frameworks.

- Experience with vector databases and search platforms such as Pinecone, Weaviate, Milvus, OpenSearch, Elasticsearch, pgvector, or similar technologies.

- Experience with cloud AI services, secure enterprise integrations, identity and access controls, and data privacy requirements.

- Exposure to healthcare, life sciences, pharma data, commercial analytics, or regulated enterprise environments.

📌 Consultant - Backend & Agentic AI Engineering (Hyderabad)
🏢 CHRYSELYS
📍 Hyderabad