Defining Both Roles Precisely

The confusion exists because “AI” is an umbrella term that covers everything from machine learning research to a developer who added a ChatGPT API call to a web app. Breaking it down:

LLM Developer (Large Language Model Developer): A developer who builds products and systems using pre-trained large language models — primarily through APIs (OpenAI, Anthropic, Google Gemini) or locally hosted models (Llama, Mistral). Their work is at the application layer: prompt engineering, RAG pipelines, agent systems, LLM orchestration, cost optimisation, and evaluation frameworks. They typically work in Python, use LangChain/LlamaIndex/custom orchestration, and deploy via standard cloud infrastructure. They do NOT train models from scratch.

AI Developer (broader definition): A developer working in artificial intelligence — which can mean ML engineering (building and training models), data science (statistical modelling and analytics), computer vision engineering (image/video AI), NLP engineering (language models), or AI infrastructure (GPU clusters, model serving). The term is so broad that it is nearly useless without a specialisation qualifier.

What Each Role Actually Builds

LLM Developer builds:

• Conversational AI chatbots and assistants using GPT-4o / Claude / Gemini APIs
• RAG (Retrieval-Augmented Generation) pipelines for document Q&A and knowledge bases
• AI agents with tool use, function calling, and multi-step reasoning
• Prompt engineering and evaluation frameworks (RAGAS, custom evals)
• LLM cost optimisation — model selection, caching, batching, prompt compression
• AI feature integration into existing web/mobile products
• Voice AI pipelines (STT → LLM → TTS)

AI Developer (ML Engineer) builds:

• Custom ML models trained on your proprietary data
• Recommendation systems, fraud detection, predictive analytics
• Computer vision models (object detection, image classification, OCR)
• Time series forecasting models
• Feature engineering pipelines for structured data
• Model serving infrastructure (FastAPI model serving, TorchServe, Triton)
• MLflow / MLOps pipelines for model training, versioning, and deployment

Skills Matrix

Which Do You Actually Need?

You need an LLM Developer if:

• “We want to add an AI assistant to our product” — LLM API integration
• “We want our AI to answer questions about our documents/data” — RAG engineer
• “We want to build an AI agent that takes actions” — LLM + tool use
• “Our GPT-4 costs are too high, we need to optimise” — LLM cost engineering
• “We want to build an AI feature in the next 2 months” — LLM developers work fast

You need an ML Engineer if:

• “We have proprietary data and want a custom model trained on it” — ML engineering
• “We need to detect fraud/anomalies in real-time from structured data” — ML + feature engineering
• “We want computer vision — detect objects in images/video” — CV engineering
• “We want to reduce LLM API costs by running our own small fine-tuned model” — fine-tuning specialist
• “We have a recommendation engine that isn’t working well” — ML engineering + data science

Compensation — 2026 Rate Benchmarks (India-Based)

How to Interview Each Role

LLM Developer — the questions that reveal real depth:

• “Walk me through how you would design a RAG system for a 50,000-document knowledge base. What chunking strategy would you use and why?”
• “Our LLM API costs are 2,000/month. What are your first 3 steps to reduce this?”
• “How do you evaluate whether your RAG system is giving accurate answers? What metrics do you track?”

ML Engineer — the questions that reveal real depth:

• “Walk me through how you would handle class imbalance in a fraud detection dataset where fraud is 0.1% of transactions.”
• “Our model performance degrades over time in production. How do you detect and address data drift?”
• “How would you reduce the inference latency of a PyTorch model from 200ms to under 50ms?”