GenAI Engineer Job Roles 2026: Titles, Skills & Responsibilities

The GenAI engineering landscape has crystallized into six distinct roles: 1. AI Agent Engineer — Builds autonomous AI agents using LangGraph, CrewAI, and tool-calling frameworks. Focuses on multi-step reasoning, tool integration, and agent orchestration. 2. RAG Engineer — Specializes in retrieval-augmented generation systems. Expert in vector databases, chunking strategies, hybrid search, re-ranking, and evaluation with RAGAS. 3. LLM Engineer — Works on prompt engineering, fine-tuning, model selection, and LLM optimization. Bridges the gap between foundation models and applications. 4. AI Solutions Architect — Designs end-to-end AI systems for enterprises. Combines knowledge of agents, RAG, deployment, and business requirements. 5. MLOps/LLMOps Engineer — Handles deployment, monitoring, and scaling of AI systems. Expert in Docker, Kubernetes, CI/CD for ML, and LangSmith/similar tools. 6. AI Product Engineer — Full-stack engineer who builds user-facing AI products. Combines frontend, backend, and AI skills to ship complete applications.

Each role emphasizes different skills: AI Agent Engineer: LangGraph (must-have), CrewAI/AutoGen, tool calling, MCP protocol, state management, multi-agent patterns, agent evaluation RAG Engineer: Vector databases (ChromaDB, Pinecone, Weaviate), embedding models, chunking strategies, hybrid search (BM25 + dense), re-ranking, RAGAS evaluation, document processing LLM Engineer: Prompt engineering, fine-tuning (LoRA, QLoRA), model evaluation, context window optimization, output parsing, structured outputs, model routing AI Solutions Architect: System design, cloud architecture (AWS/GCP), cost optimization, security, compliance, technical leadership, cross-team communication MLOps/LLMOps Engineer: Docker, Kubernetes, FastAPI, CI/CD, LangSmith, monitoring, A/B testing, model versioning, infrastructure as code AI Product Engineer: React/Next.js, Python/FastAPI, database design, streaming, WebSockets, UI/UX for AI products, authentication, billing

Salary ranges for mid-level (3-5 years experience) GenAI engineers in India: AI Agent Engineer: INR 25-35 LPA — Highest demand, fewest qualified candidates RAG Engineer: INR 20-30 LPA — Strong demand, especially in enterprise LLM Engineer: INR 22-32 LPA — Growing demand with model fine-tuning needs AI Solutions Architect: INR 35-50 LPA — Highest salary, requires breadth + depth MLOps/LLMOps Engineer: INR 20-28 LPA — Critical role, often understaffed AI Product Engineer: INR 22-35 LPA — Versatile, strong demand in startups All roles command a 30-50% premium over equivalent non-AI engineering roles. The premium is highest for AI Agent Engineers due to severe supply shortage.

Choose based on your background and interests: If you love building complex workflows and autonomous systems → AI Agent Engineer If you enjoy data, search, and information retrieval → RAG Engineer If you're fascinated by language models and fine-tuning → LLM Engineer If you enjoy architecture, system design, and leadership → AI Solutions Architect If you love DevOps, infrastructure, and reliability → MLOps/LLMOps Engineer If you want to build user-facing products end-to-end → AI Product Engineer The most versatile starting point is AI Agent Engineer because it touches all other roles — agents use RAG, call LLMs, need deployment, and serve products. From there, you can specialize based on what excites you most.

What to expect in interviews for each role: AI Agent Engineer: Design a multi-agent customer support system. Implement a tool-calling agent with error recovery. Explain LangGraph state management and checkpointing. RAG Engineer: Design a RAG pipeline for a legal document corpus. Compare chunking strategies for different document types. Implement hybrid search with BM25 and vector similarity. LLM Engineer: Compare fine-tuning approaches (full vs LoRA vs prefix tuning). Optimize a prompt for structured JSON output. Design a model evaluation pipeline. AI Solutions Architect: Design an enterprise AI platform that serves 10,000 users. Handle data privacy and compliance for a healthcare AI system. Estimate costs for a production RAG deployment. All roles: Be prepared to write Python code live, explain your past projects in depth, discuss trade-offs in AI system design, and demonstrate understanding of the full AI stack from model to deployment.