Role Overview

Experience Requirements

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