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Top 10 AI/ML Roles in Demand Across India — 2026

The AI hiring market in India has matured from experimental to critical infrastructure. In 2024, companies hired AI engineers out of FOMO. In 2026, they hire them because their product literally doesn't work without them. Here are the 10 roles we're placing most — and what hiring companies actually want.

1. LLM / GenAI Engineer

The hottest role in the market. Companies building on top of GPT-4, Gemini, Llama and Claude need engineers who understand fine-tuning, RAG architecture, prompt engineering at scale, and LLM evaluation. This role barely existed 24 months ago.

Must-have skills: LangChain/LlamaIndex, vector databases (Pinecone, Weaviate, pgvector), RAG pipelines, OpenAI/Anthropic APIs, Python

CTC range: 28–70 LPA | Demand: 🔥 Extremely High

2. MLOps Engineer

The gap between model development and production deployment is where most ML projects die. MLOps engineers who can build and maintain ML pipelines, monitoring, versioning and serving infrastructure are in massive demand.

Must-have skills: MLflow, Kubeflow, Airflow, Docker/K8s, model serving (Triton, TorchServe), feature stores

CTC range: 22–50 LPA | Demand: 🔥 Very High

3. AI Product Manager

A new category that's emerged in the last 18 months. AI PMs need to understand model capabilities and limitations well enough to write specs, evaluate outputs and make product trade-offs. Half PM, half technical translator.

Must-have skills: Prompt engineering basics, model evaluation, A/B testing, product intuition, data literacy

CTC range: 30–65 LPA | Demand: ⭐ High

4–10: Data Scientist, Computer Vision, NLP, AI Infrastructure, RL Engineer, AI Safety, Data Analyst (AI-augmented)

The remaining high-demand roles span across Computer Vision (strong demand from HealthTech and Manufacturing), NLP specialists for vernacular Indian language models, AI Infrastructure engineers who can manage GPU clusters and inference costs, and increasingly Reinforcement Learning engineers for recommendation and optimization systems.

One role worth calling out separately: AI-augmented Data Analysts. These are not data scientists — they're analysts who are deeply proficient with Copilot, Claude, and Python-based AI tooling to multiply their output. Demand has grown 3x in 12 months, driven by the realization that you can 10x your analytics function without 10x-ing headcount.

The common thread: All of these roles require strong Python fundamentals and comfort working with probabilistic, non-deterministic systems. The talent pool is thin. If you find someone great, move fast.

RD
Ravi Dubey
Co-Founder & CEO, HyrEzy Talent Solutions LLP
17+ years in tech recruitment. Hirist certified. 858K+ candidate database. Specialist in DevOps, AI/ML, Product Engineering, HealthTech and E-Commerce hiring across India.

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